All charts

## Load libraries
library(covid19)
library(ggplot2)
library(lubridate)
library(dplyr)
library(ggplot2)
library(sp)
library(raster)
library(viridis)
library(ggthemes)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(RColorBrewer)
library(readr)
library(zoo)
library(tidyr)
options(scipen = '999')

pd <- df_country %>%
  filter(country == 'India')

ggplot(data = pd,
       aes(x = date,
           y = cases_non_cum)) +
  geom_point() +
  geom_segment(aes(xend = date, yend = 0)) +
  theme_simple() +
  labs(x = 'Date',
       y = 'Incident cases',
       title = 'INDIA: Confirmed COVID-19 cases')

pd <- esp_df %>%
  filter(ccaa %in% c('Cataluña', 'Madrid'))
ggplot(data = pd,
       aes(x = date,
           y = cases_non_cum)) +
  geom_point() +
  geom_segment(aes(xend = date, yend = 0)) +
  theme_simple() +
  labs(x = 'Date',
       y = 'Incident cases',
       title = 'Confirmed COVID-19 cases') +
  facet_wrap(~ccaa)

# Number of Africa countries
pd <- df_country
pd <- pd %>% left_join(world_pop) %>%
  filter(sub_region %in% c('Sub-Saharan Africa')) %>%
  filter(cases > 0)
pd <- pd %>% group_by(date) %>% tally

ggplot(data = pd,
       aes(x = date,
           y = n)) +
  # geom_line() +
  geom_area(fill = 'darkorange',
            color = 'black',
            alpha = 0.6) +
  theme_simple() +
  labs(x = 'Date',
       y = 'Countries',
       title = 'Sub-Saharan African countries with confirmed COVID-19 cases')

# Overall Africa cases
pd <- df_country
pd <- pd %>% left_join(world_pop) %>%
  filter(sub_region %in% c('Sub-Saharan Africa'))
x = pd %>%
  group_by(date) %>%
  summarise(cases = sum(cases),
            deaths = sum(deaths),
            n = length(country[cases > 0]))

x %>% filter(date == '2020-04-07' | date == max(date) | date == '2020-03-13')

# A tibble: 3 x 4
  date       cases deaths     n
  <date>     <dbl>  <dbl> <int>
1 2020-03-13    45      0    10
2 2020-04-07  5634    106    42
3 2020-05-06 30408    735    43

# Tests
pd <- testing
entity_split <- strsplit(pd$Entity, split = ' - ')
pd$country <- unlist(lapply(entity_split, function(x){x[1]}))
pd$key <- unlist(lapply(entity_split, function(x){x[2]}))
# pd <- pd %>% filter(key == 'tests performed')
pd <- pd %>% left_join(world_pop)# %>%
  # filter(sub_region %in% c('Sub-Saharan Africa', 'Southern Europe', 'Northern Europe', 'Western Europe'))

ggplot(data = pd,
       aes(x = Date,
           y = `Cumulative total`,
           group = country)) +
  geom_line(aes(color = country))

x = pd %>% group_by(country) %>%
  filter(Date == max(Date)) %>%
  ungroup %>%
  mutate(sub_region = ifelse(sub_region == 'Sub-Saharan Africa',
                             'SSA', 'Other')) %>%
  filter(!is.na(sub_region)) %>%
  arrange(`Cumulative total`)
x$sub_region <- factor(x$sub_region, levels = unique(x$sub_region))
x$country <- factor(x$country, levels = unique(x$country))
ggplot(data = x,
       aes(x = country,
           y = `Cumulative total`)) +
  geom_bar(aes(fill = sub_region), stat = 'identity') +
  theme_simple() +
  scale_fill_manual(name = '',
                    values = c('grey', 'darkorange')) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x= 'Country',
       title = 'Tests administered')

pd = esp_df %>%
  left_join(esp_pop) %>%
  mutate(p = deaths_non_cum / pop * 1000000)

ggplot(data = pd,
       aes(x = date,
           y = p)) +
  # geom_step() +
  geom_bar(stat = 'identity',
           fill = 'red',
           alpha = 0.6,
           color = NA) +
  # geom_ribbon(aes(x = date, ymin = 0, ymax = p), data = pd, fill = 'blue') +
  facet_wrap(~ccaa) +
  theme_minimal() +
  labs(x = 'Date',
       y = 'Daily deaths per 1,000,000',
       title = 'Daily deaths per 1,000,000 population')

Daily cases Spain

pd <- df_country %>%
  filter(country == 'Spain')

ggplot(data = pd,
       aes(x = date,
           y = cases_non_cum)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  labs(x = 'Fecha',
       y = 'Casos diarios',
       title = 'Casos confirmados nuevos')

Daily deahts in Spain

pd <- df_country %>%
  filter(country == 'Spain')

ggplot(data = pd,
       aes(x = date,
           y = deaths_non_cum)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  labs(x = 'Fecha',
       y = 'Muertes diarias',
       title = 'Muertes')

Daily cases world / population-adjusted

covid19::plot_day_zero(countries = c('Italy', 'Spain', 'US', 'Germany',
                                     'Canada', 'UK', 'Netherlands'
                                     ),
                       ylog = F,
                       day0 = 1,
                       cumulative = F,
                       calendar = T,
                       pop = T,
                       point_alpha = 0,
                       color_var = 'geo')

Cases per pop last week

pd <- df_country %>%
  left_join(world_pop %>% dplyr::select(iso, pop)) %>%
  group_by(country) %>%
  mutate(max_date = max(date)) %>%
  mutate(week_ago = max_date - 6) %>%
  # filter(date == max(date)) %>%
  filter(date >= week_ago, date <= max_date) %>%
  group_by(country) %>%
  summarise(y = sum(cases_non_cum),
            pop = dplyr::first(pop),
            date_range = paste0(min(date), ' - ', max(date)),
            yp = sum(cases_non_cum) / dplyr::first(pop) * 1000000) %>%
  ungroup %>%
  filter(pop > 1000000) %>%
  arrange(desc(yp)) %>%
  head(15) %>%
  mutate(country = ifelse(country == 'United Kingdom', 'UK', country))
pd$country <- factor(pd$country, levels = unique(pd$country))

ggplot(data = pd,
       aes(x = country,
           y = yp)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12)) +
  geom_text(aes(label = round(yp, digits = 0)),
            nudge_y = -50,
            color = 'white') +
  labs(x = '',
       y = 'Cases per 1,000,000 (last 7 days)',
       title = 'New confirmed COVID-19 cases per million population, last 7 days')

Deaths per pop last week

pd <- df_country %>%
  left_join(world_pop %>% dplyr::select(iso, pop)) %>%
  group_by(country) %>%
  mutate(max_date = max(date)) %>%
  mutate(week_ago = max_date - 6) %>%
  # filter(date == max(date)) %>%
  filter(date >= week_ago, date <= max_date) %>%
  group_by(country) %>%
  summarise(y = sum(deaths_non_cum),
            pop = dplyr::first(pop),
            date_range = paste0(min(date), ' - ', max(date)),
            yp = sum(deaths_non_cum) / dplyr::first(pop) * 1000000) %>%
  ungroup %>%
  filter(pop > 1000000) %>%
  arrange(desc(yp)) %>%
  head(10) %>%
  mutate(country = ifelse(country == 'United Kingdom', 'UK', country))
pd$country <- factor(pd$country, levels = unique(pd$country))

ggplot(data = pd,
       aes(x = country,
           y = yp)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12)) +
  geom_text(aes(label = round(yp, digits = 0)),
            nudge_y = -10,
            color = 'white') +
  labs(x = '',
       y = 'Deaths per 1,000,000 (last 7 days)',
       title = 'New confirmed COVID-19 deaths per million population, last 7 days')

Lombardia, Catalonia, Madrid

New cases in last week

pd <- esp_df %>%
  left_join(esp_pop %>% dplyr::select(ccaa, pop)) %>%
  mutate(country = 'Spain') %>%
  bind_rows(
    ita %>% left_join(ita_pop %>% dplyr::select(ccaa, pop)) %>% mutate(country = 'Italy')
  ) %>%
  bind_rows(
    df %>% filter(country == 'US') %>% mutate(ccaa = district) %>% left_join(regions_pop %>% dplyr::select(ccaa, pop)) %>% mutate(country = 'US')) %>%
  group_by(ccaa) %>%
  mutate(max_date = max(date)) %>%
  mutate(week_ago = max_date - 6) %>%
  # filter(date == max(date)) %>%
  filter(date >= week_ago, date <= max_date) %>%
  group_by(ccaa) %>%
  summarise(y = sum(cases_non_cum),
            country = dplyr::first(country),
            pop = dplyr::first(pop),
            date_range = paste0(min(date), ' - ', max(date))) %>%
  ungroup %>%
  mutate(yp = y / pop * 1000000) %>%
  ungroup %>%
  # filter(pop > 1000000) %>%
  arrange(desc(yp)) 

#Get country totals
pd %>%
  group_by(country) %>%
  summarise(y = sum(y, na.rm = T),
            pop = sum(pop, na.rm = T)) %>%
  ungroup %>%
  mutate(yp = y / pop * 1000000)

# A tibble: 3 x 4
  country      y       pop    yp
  <chr>    <dbl>     <dbl> <dbl>
1 Italy    10866  60491453  180.
2 Spain    16112  47026208  343.
3 US      188707 328239523  575.

library(knitr)
library(kableExtra)
pd <- pd %>%
  mutate(yp = round(yp, digits = 1)) %>%
  mutate(Rank = 1:nrow(pd)) %>%
  dplyr::select(Rank, 
                Región = ccaa,
                `Casos nuevos, 7 días` = y,
                Población = pop,
                `Casos nuevos 7 días por millón` = yp)
kable(pd) %>%
  kable_styling("striped", full_width = F) %>%
  column_spec(which(names(pd) == 'Casos nuevos 7 días por millón'), bold = T) %>%
  row_spec(which(pd$`Región` %in% esp_df$ccaa), bold = T, color = "white", background = "#D7261E")

Rank	Región	Casos nuevos, 7 días	Población	Casos nuevos 7 días por millón
1	District of Columbia	1355	705749	1919.9
2	Rhode Island	1958	1059361	1848.3
3	New Jersey	15525	8882190	1747.9
4	Massachusetts	11760	6892503	1706.2
5	Nebraska	2838	1934408	1467.1
6	Illinois	17874	12671821	1410.5
7	Navarra	870	654214	1329.8
8	New York	24287	19453561	1248.5
9	CLM	2496	2032863	1227.8
10	Maryland	7314	6045680	1209.8
11	Connecticut	4228	3565287	1185.9
12	Delaware	1123	973764	1153.3
13	Iowa	3561	3155070	1128.7
14	CyL	2225	2399548	927.3
15	Kansas	2154	2913314	739.4
16	Minnesota	3935	5639632	697.7
17	Indiana	4688	6732219	696.4
18	Pennsylvania	8473	12801989	661.9
19	País Vasco	1440	2207776	652.2
20	Mississippi	1855	2976149	623.3
21	Virginia	5295	8535519	620.3
22	Louisiana	2739	4648794	589.2
23	Colorado	3074	5758736	533.8
24	Tennessee	3572	6829174	523.1
25	New Mexico	1078	2096829	514.1
26	New Hampshire	683	1359711	502.3
27	Michigan	4780	9986857	478.6
28	Piemonte	2078	4376000	474.9
29	Georgia	4968	10617423	467.9
30	South Dakota	407	884659	460.1
31	Liguria	662	1557000	425.2
32	Lombardia	4235	10040000	421.8
33	Aragón	545	1319291	413.1
34	Wisconsin	2381	5822434	408.9
35	La Rioja	124	316798	391.4
36	North Dakota	290	762062	380.5
37	Ohio	4273	11689100	365.6
38	Alabama	1779	4903185	362.8
39	Arizona	2498	7278717	343.2
40	Utah	1098	3205958	342.5
41	Cataluña	2562	7675217	333.8
42	Kentucky	1397	4467673	312.7
43	California	11869	39512223	300.4
44	Galicia	747	2699499	276.7
45	North Carolina	2873	10488084	273.9
46	Missouri	1663	6137428	271.0
47	Emilia-Romagna	1202	4453000	269.9
48	Asturias	271	1022800	265.0
49	Texas	7671	28995881	264.6
50	Nevada	807	3080156	262.0
51	Extremadura	270	1067710	252.9
52	C. Valenciana	1253	5003769	250.4
53	Washington	1835	7614893	241.0
54	Cantabria	137	581078	235.8
55	Trentino-Alto Adige	247	1070000	230.8
56	Madrid	1536	6663394	230.5
57	Ceuta	19	84777	224.1
58	Florida	4809	21477737	223.9
59	South Carolina	1054	5148714	204.7
60	Murcia	298	1493898	199.5
61	Oklahoma	729	3956971	184.2
62	Valle d’Aosta	22	126202	174.3
63	Wyoming	86	578759	148.6
64	Maine	198	1344212	147.3
65	Andalucía	1214	8414240	144.3
66	Arkansas	418	3017804	138.5
67	Marche	211	1532000	137.7
68	Veneto	654	4905000	133.3
69	Idaho	206	1787065	115.3
70	Oregon	470	4217737	111.4
71	Toscana	365	3737000	97.7
72	Abruzzo	124	1315000	94.3
73	Melilla	7	86487	80.9
74	Lazio	450	5897000	76.3
75	Vermont	46	623989	73.7
76	West Virginia	132	1792147	73.7
77	Friuli Venezia Giulia	84	1216000	69.1
78	Baleares	68	1149460	59.2
79	Basilicata	33	567118	58.2
80	Puglia	167	4048000	41.3
81	Sicilia	141	5027000	28.0
82	Alaska	17	731545	23.2
83	Molise	7	308493	22.7
84	Campania	122	5827000	20.9
85	Sardegna	29	1648000	17.6
86	Umbria	13	884640	14.7
87	Canarias	30	2153389	13.9
88	Calabria	20	1957000	10.2
89	Hawaii	13	1415872	9.2
90	Montana	5	1068778	4.7
91	American Samoa	0	NA	NA
92	Diamond Princess	0	NA	NA
93	Grand Princess	0	NA	NA
94	Guam	8	NA	NA
95	Northern Mariana Islands	1	NA	NA
96	Puerto Rico	535	NA	NA
97	Recovered	0	NA	NA
98	United States Virgin Islands	6	NA	NA
99	US	1	NA	NA
100	Virgin Islands	9	NA	NA
101	Wuhan Evacuee	4	NA	NA
102	NA	2	NA	NA

Just Italy and Spain

xpd = pd %>% filter(`Región` %in% c(ita$ccaa, esp_df$ccaa))
xpd$Rank <- 1:nrow(xpd)

kable(xpd) %>%
  kable_styling("striped", full_width = F) %>%
  column_spec(which(names(xpd) == 'Casos nuevos 7 días por millón'), bold = T) %>%
  row_spec(which(xpd$`Región` %in% esp_df$ccaa), bold = T, color = "white", background = "#D7261E")

Rank	Región	Casos nuevos, 7 días	Población	Casos nuevos 7 días por millón
1	Navarra	870	654214	1329.8
2	CLM	2496	2032863	1227.8
3	CyL	2225	2399548	927.3
4	País Vasco	1440	2207776	652.2
5	Piemonte	2078	4376000	474.9
6	Liguria	662	1557000	425.2
7	Lombardia	4235	10040000	421.8
8	Aragón	545	1319291	413.1
9	La Rioja	124	316798	391.4
10	Cataluña	2562	7675217	333.8
11	Galicia	747	2699499	276.7
12	Emilia-Romagna	1202	4453000	269.9
13	Asturias	271	1022800	265.0
14	Extremadura	270	1067710	252.9
15	C. Valenciana	1253	5003769	250.4
16	Cantabria	137	581078	235.8
17	Trentino-Alto Adige	247	1070000	230.8
18	Madrid	1536	6663394	230.5
19	Ceuta	19	84777	224.1
20	Murcia	298	1493898	199.5
21	Valle d’Aosta	22	126202	174.3
22	Andalucía	1214	8414240	144.3
23	Marche	211	1532000	137.7
24	Veneto	654	4905000	133.3
25	Toscana	365	3737000	97.7
26	Abruzzo	124	1315000	94.3
27	Melilla	7	86487	80.9
28	Lazio	450	5897000	76.3
29	Friuli Venezia Giulia	84	1216000	69.1
30	Baleares	68	1149460	59.2
31	Basilicata	33	567118	58.2
32	Puglia	167	4048000	41.3
33	Sicilia	141	5027000	28.0
34	Molise	7	308493	22.7
35	Campania	122	5827000	20.9
36	Sardegna	29	1648000	17.6
37	Umbria	13	884640	14.7
38	Canarias	30	2153389	13.9
39	Calabria	20	1957000	10.2

x = esp_df %>% left_join(esp_pop) %>%
  bind_rows(ita %>% left_join(ita_pop)) %>%
  filter(date == '2020-04-09') %>%
  filter(ccaa %in% c('Madrid',
                     'Cataluña', 'Lombardia')) %>%
  dplyr::select(ccaa, deaths, cases, pop) %>%
  mutate(deathsp = deaths / pop * 100000,
         casesp = cases / pop * 100000)


covid19::plot_day_zero(countries = c('Italy', 'Spain', 'China', 'South Korea', 'Sinagpore'),
                       districts = c('Madrid', #'Hubei',
                     'Cataluña', 'Lombardia'),
                     by_district = T,
                     roll = 7,
                     deaths = F,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Casos diaris (mitjana mòbil de 7 dies)',
       title = 'Casos diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') 

covid19::plot_day_zero(countries = c('Italy', 'Spain'),
                     roll = 7,
                     deaths = F,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Casos diaris per 1.000.000 (mitjana mòbil de 7 dies)',
       title = 'Casos diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') +
  theme(legend.direction = 'horizontal',
        legend.position = 'top')

covid19::plot_day_zero(countries = c('Italy', 'Spain'),
                     roll = 7,
                     deaths = T,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Morts diaris per 1.000.000 (mitjana mòbil de 7 dies)',
       title = 'Morts diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') +
  theme(legend.direction = 'horizontal',
        legend.position = 'top')

covid19::plot_day_zero(countries = c('Italy', 'Spain'),
                       by_district = T,
                       districts = c('Madrid', 'Emilia-Romagna',
                     'Cataluña', 'Lombardia'),
                     roll = 7,
                     deaths = F,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Casos diaris per 1.000.000 (mitjana mòbil de 7 dies)',
       title = 'Casos diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') +
  theme(legend.direction = 'horizontal',
        legend.position = 'top')

Asia

covid19::plot_day_zero(countries = c('Japan', 'South Korea', 'Singapore', 'Hong Kong'),
                     roll = 7,
                     deaths = F,
                     # pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
    labs(x = 'Data',
       y = 'Casos diaris (mitjana mòbil de 3 dies)',
       title = 'Casos diaris',
       subtitle = 'Mitjana mòbil de 3 dies') +
  facet_wrap(~geo, scales = 'free_y') +
  theme(legend.position = 'none')

Day of week analysis

pd <- esp_df %>%
  arrange(date) %>%
  group_by(date) %>%
  summarise(deaths_non_cum = sum(deaths_non_cum),
            cases_non_cum = sum(cases_non_cum)) %>%
  ungroup %>%  
  mutate(dow = weekdays(date)) %>%
  mutate(week = isoweek(date)) %>%
  group_by(week) %>%
  mutate(start_date = min(date)) %>%
  ungroup %>%
  filter(date >= '2020-03-09')
pd$dow <- factor(pd$dow,
                 levels = c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday',
                            'Saturday', 'Sunday'),
                 labels = c('Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes',
                            'Sábado', 'Domingo'))
n_cols <- length(unique(pd$start_date))
cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 9, name = 'Spectral'))(n_cols)
pd$start_date <- factor(pd$start_date)
ggplot(data = pd,
       aes(x = dow,
           y = cases_non_cum,
           group = week,
           color = start_date)) +
  geom_line(size = 4) +
  geom_point(size = 4) +
  scale_color_manual(name = 'Primer día\nde la semana',
                     values = cols) +
  theme_simple() +
  labs(x = 'Día de la semana',
       y = 'Muertes')

Basque country vs rest of Spain

pd <- esp_df %>%
  mutate(geo = ifelse(ccaa == 'País Vasco', 'Basque country', 'Rest of Spain')) %>%
  group_by(geo, date) %>%
  summarise(deaths = sum(deaths)) %>%
  ungroup
pp <- esp_pop %>%
    mutate(geo = ifelse(ccaa == 'País Vasco', 'Basque country', 'Rest of Spain')) %>%
  group_by(geo) %>%
  summarise(pop = sum(pop))
pd <- left_join(pd, pp) %>% mutate(pk = deaths / pop * 100000)

ggplot(data = pd %>% filter(pk > 0.1),
       aes(x = date,
           y = pk,
           color = geo)) +
  geom_line() +
  labs(x = 'Date',
       y = 'Cumulative deaths per 100,000') +
  scale_color_manual(name = '',
                     values = c('red', 'black')) +
  theme_simple() 

Country trajectories, population adjusted

countries <- c(
  'Spain',
  'US',
  'France',
  # 'Portugal',
  'Italy',
  'China'
)
districts <- c('Lombardia', 'Cataluña', 
               'New York', 
               # 'Hubei',
               'CyL', 
               'CLM', 
               # 'Washington',
               'La Rioja',
               'Madrid')

plot_day_zero(countries = countries,
              districts = districts,
              ylog = F,
              day0 = 1,
              cumulative = F,
              time_before = 0,
              roll = 3,
              deaths = T,
              pop = T,
              by_district = T,
              point_alpha = 0,
              line_size = 3,
              color_var = 'geo')

Italy and Spain

dir.create('/tmp/totesmou')
plot_day_zero(countries = c('Spain', 'Italy', max_date = Sys.Date()-1),
              point_size = 2, calendar = T)

ggsave('/tmp/totesmou/1_italy_vs_spain.png',
       height = 5.6,
       width = 9.6)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F)

ggsave('/tmp/totesmou/2_italy_vs_spain_temps_ajustat.png',
       height = 5.6,
       width = 9.6)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = T, deaths = T, day0 = 10)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F, deaths = T, day0 = 10)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F, deaths = T, day0 = 10, pop = T)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F, deaths = T, day0 = 1, pop = T, roll = 3, roll_fun = 'mean')

plot_day_zero(countries = c('Spain', 'Italy'),
              districts = c('Cataluña', 'Madrid',
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              day0 = 10,
              pop = T)

plot_day_zero(countries = c('Spain', 'Italy'),
              districts = c('Cataluña', 'Madrid',
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              day0 = 1,
              pop = T)

plot_day_zero(countries = c('Spain', 'Italy'),
              districts = c('Cataluña', 'Madrid',
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              roll = 3,
              roll_fun = 'mean',
              day0 = 1,
              ylog = F,
              pop = T, calendar = T)

plot_day_zero(countries = c('Spain', 'Italy', 'US'),
              districts = c('Cataluña', 'Madrid', 
                            'New York', 
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              roll = 3,
              roll_fun = 'mean',
              day0 = 1,
              pop = T)

plot_day_zero(countries = c('Spain', 'Italy', 'US'),
              districts = c('Cataluña', 'Madrid', 
                            'New York', 
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              # roll = 7,
              roll_fun = 'mean',
              day0 = 1,
              pop = F)

plot_day_zero(color_var = 'iso', by_district = T,
              deaths = T,
              day0 = 1,
              alpha = 0.6,
              point_alpha = 0,
              calendar = T,
              countries = c('Spain', 'Italy'),
              pop = T)

place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Altres CCAA')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Altres regions italianes')
  # )
}
pd <- esp_df %>% mutate(country = 'Espanya') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Itàlia')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = c('Madrid',
                             # 'Cataluña',
                             'Altres CCAA',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Altres regions italianes'))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)

label_data <- pd %>%
  filter(country  %in% c('Itàlia', 'Espanya')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -9))
ggplot(data = pd %>% group_by(country) %>% mutate(start_day = dplyr::first(date[deaths >=50])) %>% filter(date >= start_day),
       aes(x = date,
           y = p_deaths_country,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  # geom_point(size = 3,
  #            aes(color = ccaa)) +
  facet_wrap(~country) +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Data',
       y = 'Percentatge de morts',
       title = 'Percentatge de morts: regió més afectada vs resta del pais',
       subtitle = 'A partir del primer día a cada país amb 50 morts acumulades') +
  theme(legend.position = 'top',
        strip.text = element_text(size = 30),
        legend.text = element_text(size = 10))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5)) +
  geom_text(data = label_data,
            aes(x = x-2,
                y = y,
                label = label,
                color = ccaa),
            size = 7,
            show.legend = FALSE)

# Same cahrt as previous, but one shared axis
place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Rest of Spain')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Rest of Italy')
  # )
}
pd <- esp_df %>% mutate(country = 'Spain') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Italy')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = c('Madrid',
                             # 'Cataluña',
                             'Rest of Spain',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Rest of Italy'))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)

label_data <- pd %>%
  filter(country  %in% c('Italy', 'Spain')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  # mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -9)) %>%
  ungroup
ggplot(data = pd %>% group_by(country) %>% mutate(start_day = dplyr::first(date[deaths >=30])) %>% filter(date >= start_day),
       aes(x = date,
           y = p_deaths_country,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  # geom_point(size = 3,
  #            aes(color = ccaa)) +
  # facet_wrap(~country, scales = 'free_x') +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Date',
       y = 'Percentage of deaths',
       title = 'Percentage of country\'s cumulative COVID-19 deaths by geography',
       subtitle = 'Starting at first day with 50 or more cumulative deaths') +
  theme(legend.position = 'top',
        strip.text = element_text(size = 30),
        legend.text = element_text(size = 10))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5))# +

  # geom_text(data = label_data,
  #           aes(x = x-2,
  #               y = y,
  #               label = label,
  #               color = ccaa),
  #           size = 7,
  #           show.legend = FALSE)

Same chart as above but absolute numbers

# Same cahrt as previous, but one shared axis
place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Rest of Spain')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Rest of Italy')
  # )
}
pd <- esp_df %>% mutate(country = 'Spain') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Italy')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = rev(c('Madrid',
                             # 'Cataluña',
                             'Rest of Spain',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Rest of Italy')))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)

label_data <- pd %>%
  filter(country  %in% c('Italy', 'Spain')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  # mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -9)) %>%
  ungroup

# Get moving
ma <- function(x, n = 2){
    
    if(length(x) >= n){
      stats::filter(x, rep(1 / n, n), sides = 1)
    } else {
      new_n <- length(x)
      stats::filter(x, rep(1 / new_n, new_n), sides = 1)
    }
}


ggplot(data = pd %>% group_by(country) %>% 
         mutate(start_day = dplyr::first(date[deaths >=1])) %>% 
         filter(date >= start_day) %>% 
         mutate(days_since = as.numeric(date - start_day)) %>%
         ungroup %>% arrange(date) %>%
         group_by(country, ccaa) %>%
         mutate(rolling_average = ma(deaths_non_cum, n = 3)) %>%
         ungroup,
       aes(x = date,
           y = rolling_average,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  # geom_point(size = 3,
  #            aes(color = ccaa)) +
  # scale_y_log10(limits = c(1.5, 1000)) +
  # scale_y_log10() +
  facet_wrap(~country) +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  # geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Date',
       y = 'Deaths (log-scale)',
       title = 'Daily COVID-19 deaths by geography',
       subtitle = '3-day rolling average') +
  theme(legend.position = 'top',
        plot.title = element_text(size = 30),
        plot.subtitle = element_text(size = 24),
        strip.text = element_text(size = 30, hjust = 0.5),
        legend.text = element_text(size = 20))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5))

ggsave('~/Desktop/madlom.png')

roll_curve <- function(data,
                       n = 4,
                       deaths = FALSE,
                       scales = 'fixed',
                       nrow = NULL,
                       ncol = NULL,
                       pop = FALSE){

  # Create the n day rolling avg
  ma <- function(x, n = 5){
    
    if(length(x) >= n){
      stats::filter(x, rep(1 / n, n), sides = 1)
    } else {
      new_n <- length(x)
      stats::filter(x, rep(1 / new_n, new_n), sides = 1)
    }
    
    
  }
  
  pd <- data
  if(deaths){
    pd$var <- pd$deaths_non_cum
  } else {
    pd$var <- pd$cases_non_cum
  }
  
  if('ccaa' %in% names(pd)){
    pd$geo <- pd$ccaa
  } else {
    pd$geo <- pd$iso
  }
  
  if(pop){
    # try to get population
    if(any(pd$geo %in% unique(esp_df$ccaa))){
      right <- esp_pop
      right_var <- 'ccaa'
    } else {
      right <- world_pop
      right_var <- 'iso'
    }
    pd <- pd %>% left_join(right %>% dplyr::select(all_of(right_var), pop),
                           by = c('geo' = right_var)) %>%
      mutate(var = var / pop * 100000)
  }
  
  pd <- pd %>%
    arrange(date) %>%
    group_by(geo) %>%
    mutate(with_lag = ma(var, n = n))
  
  
  ggplot() +
    geom_bar(data = pd,
         aes(x = date,
             y = var),
             stat = 'identity',
         fill = 'grey',
         alpha = 0.8) +
    geom_area(data = pd,
              aes(x = date,
                  y = with_lag),
              color = 'red',
              fill = 'red',
              alpha = 0.6) +
    facet_wrap(~geo, scales = scales, nrow = nrow, ncol = ncol) +
    theme_simple() +
    labs(x = '',
         y = ifelse(deaths, 'Deaths', 'Cases'),
         title = paste0('Daily (non-cumulative) ', ifelse(deaths, 'deaths', 'cases'),
                        ifelse(pop, ' (per 100,000)', '')),
         subtitle = paste0('Data as of ', max(data$date),
                           '.\nRed line = ', n, ' day rolling average. Grey bar = day-specific value.')) +
    theme(axis.text.x = element_text(size = 7,
                                     angle = 90,
                                     hjust = 0.5, vjust = 1)) #+
    # scale_x_date(name ='',
    #              breaks = sort(unique(pd$date)),
    #              labels = format(sort(unique(pd$date)), '%b %d'))
    # scale_y_log10()
}
this_ccaa <- 'Cataluña'
plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
roll_curve(plot_data, scales = 'fixed')  + theme(strip.text = element_text(size = 30))

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
roll_curve(plot_data, deaths = T, scales = 'fixed') + theme(strip.text = element_text(size = 30))

african_countries <-  world_pop$country[world_pop$sub_region %in% c('Sub-Saharan Africa')]

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 1,
                    max_date = Sys.Date() - 46,
                    ylog = F) +
  ylim(0, 5000)
pd

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 1,
                    max_date = Sys.Date(),
                    ylog = F) +
  ylim(0, 5000)  
pd

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 1,
                    calendar = T,
                    max_date = Sys.Date(),
                    ylog = T) + theme(legend.position = 'none')
pd

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 13)
pd

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 6)
pd

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 10,
                    max_date = Sys.Date(),
                    ylog = F)
pd

latam_countries <-  world_pop$country[world_pop$sub_region %in% c('Latin America and the Caribbean')]
latam_countries <- latam_countries[!latam_countries %in% c('Guyana')]

pd <- plot_day_zero(countries = c(latam_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 13)
pd

pd <- plot_day_zero(countries = c(latam_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 6)
pd

pd <- plot_day_zero(countries = c(latam_countries),
                    day0 = 10,
                    max_date = Sys.Date())
pd

Latin America and Africa vs Europe

isos <- sort(unique(world_pop$sub_region))
keep_countries <- world_pop$country[world_pop$sub_region %in% c('Latin America and the Caribbean', 'Sub-Saharan Africa') |
                                      world_pop$region %in% 'Europe']
keep_countries <- keep_countries[!keep_countries %in% c('Guyana')]
pd <- df_country %>% ungroup %>%
  filter(country %in% keep_countries) %>%
  dplyr::select(-country) %>%
  left_join(world_pop) %>%
  group_by(iso) %>%
  mutate(day0 = min(date[cases >= 10])) %>%
  ungroup %>%
  mutate(days_since = date - day0) %>%
  filter(days_since >= 0)


cols <- c( 'black')
g <- ggplot(data = pd,
       aes(x = days_since,
           y = cases,
           group = country,
           color = region)) +
  geom_line(data = pd %>% filter(region == 'Europe'),
            alpha = 0.6) +
  scale_y_log10() +
  scale_color_manual(name = '', values = cols) +
  theme_simple() +
  labs(x = 'Days since first day at 10 cases') +
  theme(legend.position = 'top')
g

cols <- c('darkred', 'black')

g + 
    geom_line(data = pd %>% filter(region == 'Africa'),
            # alpha = 1,
            size = 1.5,
            alpha = 0.8) +
    scale_y_log10() +
    scale_color_manual(name = '', values = cols) 

cols <- c( 'darkorange', 'black')
g + 
    geom_line(data = pd %>% filter(sub_region == 'Latin America and the Caribbean'),
            # alpha = 1,
            size = 1.5,
            alpha = 0.8) +
    scale_color_manual(name = '', values = cols) 

cols <- c('darkred', 'darkorange', 'black')
g + 
    geom_line(data = pd %>% filter(sub_region != 'Europe'),
            # alpha = 1,
            size = 1.5,
            alpha = 0.8) +
    scale_color_manual(name = '', values = cols) 

# Assets
pyramid_dir <- '../../data-raw/pyramids/'
pyramid_files <- dir(pyramid_dir)
out_list <- list()
for(i in 1:length(pyramid_files)){
  out_list[[i]] <- read_csv(paste0(pyramid_dir, pyramid_files[i])) %>%
    mutate(region = gsub('.csv', '', pyramid_files[i]))
}
pyramid <- bind_rows(out_list)
make_pyramid <- function(the_region = 'AFRICA-2019'){
  sub_data <- pyramid %>% filter(region == the_region)
  sub_data$Age <- factor(sub_data$Age, levels = sub_data$Age)
  sub_data <- tidyr::gather(sub_data, key, value, M:F)
  ggplot(data = sub_data,
         aes(x = Age,
             y = value,
             fill = key)) +
    geom_bar(stat = 'identity',
             position = position_dodge(),
             alpha = 0.6,
             color = 'black') +
    scale_fill_manual(name = '', values = c('darkorange', 'lightblue')) +
    theme_simple() +
    labs(x = 'Age group',
         y = 'Population') +
    theme(legend.position = 'top')
}
make_pyramid_overlay <- function(){
  sub_data <- pyramid %>% filter(region %in% c('EUROPE-2019',
                                               'AFRICA-2019',
                                               'LATIN AMERICA AND THE CARIBBEAN-2019')) %>%
    mutate(region = gsub('-2019', '', region))
   sub_data$Age <- factor(sub_data$Age, levels = unique(sub_data$Age))
  sub_data <- tidyr::gather(sub_data, key, value, M:F) %>%
    group_by(Age, region) %>%
    summarise(value = sum(value)) %>%
    ungroup %>%
    group_by(region) %>%
    mutate(p = value / sum(value) * 100) %>%
    ungroup
  ggplot(data = sub_data,
         aes(x = Age,
             y = p,
             color = region,
             group = region,
             fill = region)) +
    geom_area(position = position_dodge(),
              alpha = 0.6) +
    scale_fill_manual(name = '',
                      values = c('darkred', 'darkorange', 'black')) +
    scale_color_manual(name = '',
                      values = c('darkred', 'darkorange', 'black')) +
    theme_simple() +
    theme(legend.position = 'top') +
    labs(x = 'Age group', y = 'Percentage')
}

make_pyramid(the_region = 'Spain-2019') + labs(title = 'Spain')
make_pyramid(the_region = 'Italy-2019') + labs(title = 'Italy')

make_pyramid(the_region = 'EUROPE-2019') + labs(title = 'Europe')
make_pyramid(the_region = 'Kenya-2019') + labs(title = 'Kenya')
make_pyramid(the_region = 'Mozambique-2019') + labs(title = 'Mozambique')
make_pyramid(the_region = 'Tanzania-2019') + labs(title = 'Tanzania')

make_pyramid(the_region = 'Guatemala-2019') + labs(title = 'Guatemala')

make_pyramid(the_region = 'AFRICA-2019') + labs(title = 'Africa')

make_pyramid(the_region = 'LATIN AMERICA AND THE CARIBBEAN-2019') + labs(title = 'Latin America and the Caribbean')

make_pyramid_overlay() + labs(title = 'Population distribution by region')

pyramid <- pyramid %>%
  mutate(old = Age %in% c('80-84', '85-89', '90-94','95-99', '100+'))

pyramid %>%
  group_by(region, old) %>%
  summarise(n = sum(M) + sum(F)) %>%
  ungroup %>%
  group_by(region) %>%
  mutate(p = n / sum(n) * 100) %>%
  filter(old)

plot_day_zero(countries = c('South Africa', 'Spain'), day0 = 1, calendar = T)

plot_day_zero(countries = c('Kenya', 'Italy'), day0 = 10, calendar = F)

Pics

plot_day_zero(countries = c('China', 'Italy', 'Spain'),
              districts = c('Hubei', 'Lombardia', 'Cataluña', 'Madrid'),
              by_district = T,
              point_alpha = 0,
              day0 = 5,
              pop = F,
              deaths = T,
              ylog = T,
              calendar = F,
              roll = 5)

Map of portugal, france, spain

# cat_transform <- function(x){ifelse(x == 'Catalunya', 'Cataluña', x)}
cat_transform <- function(x){return(x)}
pd <- por_df %>% mutate(country = 'Portugal') %>%
  bind_rows(esp_df %>% mutate(country = 'Spain')) %>%
  bind_rows(fra_df %>% mutate(country = 'France')) %>%
  bind_rows(ita %>% mutate(country = 'Italy')) %>%
  bind_rows(
    df %>% filter(country == 'Andorra') %>%
      mutate(ccaa = 'Andorra')
  )
keep_date = pd %>% group_by(country) %>% summarise(max_date= max(date)) %>% summarise(x = min(max_date)) %>% .$x
pd <- pd %>%
  mutate(ccaa = cat_transform(ccaa)) %>%
  group_by(ccaa) %>%
  filter(date == keep_date) %>%
  # filter(date == '2020-03-27') %>%
  ungroup %>%
  dplyr::select(date, ccaa, deaths, deaths_non_cum,
                cases, cases_non_cum) %>%
  left_join(regions_pop %>%
              bind_rows(
                world_pop %>% filter(country == 'Andorra') %>% dplyr::mutate(ccaa = country) %>%
                  dplyr::select(-region, -sub_region)
              )) %>%
  mutate(cases_per_million = cases / pop * 1000000,
         deaths_per_million = deaths / pop * 1000000) %>%
  mutate(cases_per_million_non_cum = cases_non_cum / pop * 1000000,
         deaths_per_million_non_cum = deaths_non_cum / pop * 1000000)

map_esp1 <- map_esp
map_esp1@data <- map_esp1@data %>% dplyr::select(ccaa)
map_fra1 <- map_fra
map_fra1@data <- map_fra1@data %>% dplyr::select(ccaa = NAME_1)
map_por1 <- map_por
map_por1@data <- map_por1@data %>% dplyr::select(ccaa = CCDR)
map_ita1 <- map_ita 
map_ita1@data <- map_ita1@data %>% dplyr::select(ccaa)
map_and1 <- map_and
map_and1@data <- map_and1@data %>% dplyr::select(ccaa = NAME_0)


map <- 
  rbind(map_esp1,
        map_por1,
        map_fra1,
        map_ita1,
        map_and1)

# Remove areas not of interest
centroids <- coordinates(map)
centroids <- data.frame(centroids)
names(centroids) <- c('x', 'y')
centroids$ccaa <- map@data$ccaa
centroids <- left_join(centroids, pd)
# map <- map_sp <- map[centroids$y >35 & centroids$x > -10 &
#              centroids$x < 8 & (centroids$y < 43  | map@data$ccaa %in% c('Occitanie', 'Nouvelle-Aquitaine') |
#                                   map@data$ccaa %in% esp_df$ccaa),]
# map_sp <- map <-  map[centroids$x > -10 & centroids$y <47,]
map_sp <- map <-  map[centroids$x > -10 & centroids$y <77,]

# map_sp <- map <-  map[centroids$x > -10,]

# fortify
map <- fortify(map, region = 'ccaa')

# join data
map$ccaa <- map$id
map <- left_join(map, pd)
var <- 'deaths_per_million'
map$var <- as.numeric(unlist(map[,var]))
centroids <- centroids[,c('ccaa', 'x', 'y', var)]
centroids <- centroids %>%
  filter(ccaa %in% map_sp@data$ccaa)

# cols <- rev(RColorBrewer::brewer.pal(n = 9, name = 'Spectral'))
# cols <- c('#A16928','#bd925a','#d6bd8d','#edeac2','#b5c8b8','#79a7ac','#2887a1')
# cols <- c('#009392','#39b185','#9ccb86','#e9e29c','#eeb479','#e88471','#cf597e')
# cols <- c('#008080','#70a494','#b4c8a8','#f6edbd','#edbb8a','#de8a5a','#ca562c')
cols <- rev(colorRampPalette(c('darkred', 'red', 'darkorange', 'orange', 'yellow', 'lightblue'))(10))
g <- ggplot(data = map,
         aes(x = long,
             y = lat,
             group = group)) +
    geom_polygon(aes(fill = var),
                 lwd = 0.3,
                 # color = 'darkgrey',
                 color = NA,
                 size = 0.6) +
      scale_fill_gradientn(name = '',
                           colours = cols) +
  # scale_fill_() +
  ggthemes::theme_map() +
  theme(legend.position = 'bottom',
        plot.title = element_text(size = 16)) +
  guides(fill = guide_colorbar(direction= 'horizontal',
                               barwidth = 50,
                               barheight = 1,
                               label.position = 'bottom')) +
  labs(title = 'Cumulative COVID-19 deaths per million population, western Mediterranean',
       subtitle = paste0('Data as of ', format(max(pd$date), '%B %d, %Y'))) +
  geom_text(data = centroids,
            aes(x = x,
                y = y,
                group = NA,
                label = paste0(ccaa, '\n',
                               round(deaths_per_million, digits = 2))),
            alpha = 0.8,
            size = 3)
g

ggsave('/tmp/map_with_borders.png',
       height = 8, width = 13)

Animation, Portugal, France, Spain, Italy

dir.create('/tmp/animation_map/')
pd <- por_df %>% mutate(country = 'Portugal') %>%
  bind_rows(esp_df %>% mutate(country = 'Spain')) %>%
  bind_rows(fra_df %>% mutate(country = 'France')) %>%
  bind_rows(ita %>% mutate(country = 'Italy')) %>%
  bind_rows(
    df %>% filter(country == 'Andorra') %>%
      mutate(ccaa = 'Andorra')
  )
pd %>% group_by(country) %>% summarise(max_date= max(date))

# A tibble: 5 x 2
  country  max_date  
  <chr>    <date>    
1 Andorra  2020-05-06
2 France   2020-05-06
3 Italy    2020-05-06
4 Portugal 2020-05-06
5 Spain    2020-05-06

unique_dates <- sort(unique(pd$date))
unique_dates <- unique_dates[unique_dates >= '2020-03-01']
popper <- regions_pop %>%
                bind_rows(
                  world_pop %>% filter(country == 'Andorra') %>% dplyr::mutate(ccaa = country) %>%
                    dplyr::select(-region, -sub_region)
                )


map_esp1 <- map_esp
map_esp1@data <- map_esp1@data %>% dplyr::select(ccaa)
map_fra1 <- map_fra
map_fra1@data <- map_fra1@data %>% dplyr::select(ccaa = NAME_1)
map_por1 <- map_por
map_por1@data <- map_por1@data %>% dplyr::select(ccaa = CCDR)
map_ita1 <- map_ita 
map_ita1@data <- map_ita1@data %>% dplyr::select(ccaa)
map_and1 <- map_and
map_and1@data <- map_and1@data %>% dplyr::select(ccaa = NAME_0)


map <- 
  rbind(map_esp1,
        map_por1,
        map_fra1,
        map_ita1,
        map_and1)

# Remove areas not of interest
centroids <- coordinates(map)
centroids <- data.frame(centroids)
names(centroids) <- c('x', 'y')
centroids$ccaa <- map@data$ccaa
# map <- map_sp <- map[centroids$y >35 & centroids$x > -10 &
#              # centroids$x < 8 &
#                (centroids$y < 43  | map@data$ccaa %in% c('Occitanie', 'Nouvelle-Aquitaine') |
#                                   map@data$ccaa %in% esp_df$ccaa),]
# map_sp <- map <-  map[centroids$x > -10 & centroids$y <47,]
map_sp <- map <-  map[centroids$x > -10 & centroids$y <477,]


# fortify
map <- fortify(map, region = 'ccaa')



for(i in 1:length(unique_dates)){
  this_date <- unique_dates[i]
    today_map <- map
    today_centroids <- centroids
    today_pd <- pd

  today_pd <- today_pd %>%
      mutate(ccaa = cat_transform(ccaa)) %>%
    group_by(ccaa) %>%
    # filter(date == max(date)) %>%
    filter(date == this_date) %>%
    ungroup %>%
    dplyr::select(date, ccaa, deaths, deaths_non_cum,
                  cases, cases_non_cum) %>%
    left_join(popper) %>%
    mutate(cases_per_million = cases / pop * 1000000,
           deaths_per_million = deaths / pop * 1000000) %>%
    mutate(cases_per_million_non_cum = cases_non_cum / pop * 1000000,
           deaths_per_million_non_cum = deaths_non_cum / pop * 1000000)
  
  today_centroids <- left_join(today_centroids, today_pd)

  
  # join data
  today_map$ccaa <- today_map$id
  today_map <- left_join(today_map, today_pd)
  var <- 'deaths_per_million'
  today_map$var <- as.numeric(unlist(today_map[,var]))
  today_map$var <- ifelse(is.na(today_map$var), 0, today_map$var)
  today_centroids <- today_centroids[,c('ccaa', 'x', 'y', var)]
  today_centroids <- today_centroids %>%
    filter(ccaa %in% today_map$ccaa)
  today_centroids$var <- today_centroids[,var]
  today_centroids$var <- ifelse(is.na(today_centroids$var), 0, today_centroids$var)

  
  # cols <- rev(RColorBrewer::brewer.pal(n = 9, name = 'Spectral'))
  # cols <- c('#A16928','#bd925a','#d6bd8d','#edeac2','#b5c8b8','#79a7ac','#2887a1')
  # cols <- c('#009392','#39b185','#9ccb86','#e9e29c','#eeb479','#e88471','#cf597e')
  # cols <- c('#008080','#70a494','#b4c8a8','#f6edbd','#edbb8a','#de8a5a','#ca562c')
  cols <- rev(colorRampPalette(c('darkred', 'red', 'darkorange', 'orange', 'yellow', 'white'))(17))
  g <- ggplot(data = today_map,
           aes(x = long,
               y = lat,
               group = group)) +
      geom_polygon(aes(fill = var),
                   lwd = 0.3,
                   # color = 'darkgrey',
                   color = NA,
                   size = 0.6) +
        scale_fill_gradientn(name = '',
                             colours = cols,
                             breaks = seq(0, 1100, 50),
                             limits = c(0, 1100)) +
    # scale_fill_() +
    ggthemes::theme_map() +
    theme(legend.position = 'right',
          plot.title = element_text(size = 24)) +
    guides(fill = guide_colorbar(direction= 'vertical',
                                 barwidth = 1,
                                 barheight = 30,
                                 label.position = 'left')) +
    labs(subtitle = 'Cumulative COVID-19 deaths per million population',
         title = paste0(format(this_date, '%B %d, %Y'))) +
    geom_text(data = today_centroids,
              aes(x = x,
                  y = y,
                  group = NA,
                  label = paste0(ccaa, '\n',
                                 round(var, digits = 2))),
              alpha = 0.8,
              size = 1.5)
  
  ggsave(paste0('/tmp/animation_map/', this_date, '.png'),
         plot = g,
         height = 6, width = 9)
}

# Command line
cd /tmp/animation_map
mogrify -resize 50% *.png
convert -delay 20 -loop 0 *.png result.gif

Deaths overall over time Spain

df_country %>% filter(country == 'Spain') %>% arrange(date) %>% tail

# A tibble: 6 x 10
# Groups:   country [1]
  country date        cases deaths   uci hospitalizations cases_non_cum
  <chr>   <date>      <dbl>  <dbl> <dbl>            <int>         <dbl>
1 Spain   2020-05-01 246449  25100 10908                0          2458
2 Spain   2020-05-02 248028  25264 10973                0          1579
3 Spain   2020-05-03 249120  25428 10994                0          1092
4 Spain   2020-05-04 251081  25613 11031                0          1961
5 Spain   2020-05-05 254050  25857 11082                0          2969
6 Spain   2020-05-06 256855  26070 11140                0          2805
# … with 3 more variables: deaths_non_cum <dbl>, uci_non_cum <dbl>, iso <chr>

Deaths yesterday

pd <- df_country
pd$value <- pd$deaths_non_cum
the_date <- Sys.Date() - 1
pd <- pd %>%
  filter(date == the_date) %>%
  dplyr::select(country, iso, cases, cases_non_cum,
                deaths, value) %>%
  dplyr::arrange(desc(value)) %>%
  left_join(world_pop %>% dplyr::select(-country)) %>%
  mutate(value_per_million = value / pop * 1000000) #%>% 
  # arrange(desc(value_per_million))
pd <- pd[1:10,]
pd$country <- factor(pd$country, levels = pd$country)
ggplot(data = pd,
       aes(x = country,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  geom_text(aes(label = value),
            nudge_y = -20,
            size = 4,
            color = 'white') +
  labs(title = paste0('Confirmed COVID-19 deaths on ', the_date),
       x = '', y = '')

pd

# A tibble: 10 x 10
# Groups:   country [10]
   country iso    cases cases_non_cum deaths value    pop region sub_region
   <fct>   <chr>  <dbl>         <dbl>  <dbl> <dbl>  <dbl> <chr>  <chr>     
 1 US      USA   1.23e6         24252  73431  2367 3.27e8 Ameri… Northern …
 2 Brazil  BRA   1.27e5         11156   8588   650 2.09e8 Ameri… Latin Ame…
 3 United… GBR   2.02e5          6116  30150   649 6.65e7 Europe Northern …
 4 Italy   ITA   2.14e5          1444  29684   369 6.04e7 Europe Southern …
 5 Belgium BEL   5.08e4           272   8339   323 1.14e7 Europe Western E…
 6 Germany DEU   1.68e5          1155   7275   282 8.29e7 Europe Western E…
 7 France  FRA   1.74e5          3537  25812   275 6.70e7 Europe Western E…
 8 Spain   ESP   2.57e5          2805  26070   213 4.67e7 Europe Southern …
 9 Mexico  MEX   2.76e4          1609   2704   197 1.26e8 Ameri… Latin Ame…
10 Canada  CAN   6.47e4          1479   4366   176 3.71e7 Ameri… Northern …
# … with 1 more variable: value_per_million <dbl>

Deaths per million yesterday per CCAA

pd <- esp_df
pd$value <- pd$deaths_non_cum
the_date <- max(pd$date)
pd <- pd %>%
  filter(date == max(date)) %>%
  dplyr::select(ccaa, cases, cases_non_cum,
                deaths, value) %>%
  dplyr::arrange(desc(value)) %>%
  left_join(esp_pop)%>%
  mutate(value_per_million = value / pop * 1000000) #%>% 
  # arrange(desc(value_per_million))
pd <- pd[1:10,]
pd$ccaa <- factor(pd$ccaa, levels = pd$ccaa)
ggplot(data = pd,
       aes(x = ccaa,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  geom_text(aes(label = value),
            nudge_y = -20,
            size = 4,
            color = 'white')

pd

# A tibble: 10 x 7
   ccaa          cases cases_non_cum deaths value     pop value_per_million
   <fct>         <dbl>         <dbl>  <dbl> <dbl>   <dbl>             <dbl>
 1 Cataluña      52505           415   5394    49 7675217              6.38
 2 Madrid        68662           189   8504    38 6663394              5.70
 3 CLM           22567           401   2677    30 2032863             14.8 
 4 País Vasco    16959           280   1383    19 2207776              8.61
 5 CyL           22537           381   1864    17 2399548              7.08
 6 Andalucía     14870           231   1294    13 8414240              1.54
 7 Aragón         6516           134    800    12 1319291              9.10
 8 C. Valenciana 13311           247   1303    12 5003769              2.40
 9 Asturias       3099            26    292     5 1022800              4.89
10 Extremadura    3808            62    467     4 1067710              3.75

Deaths yesterday animation

dir.create('/tmp/animation_deaths')
dates <- seq(as.Date('2020-03-17'), (Sys.Date()-1), by = 1)
for(i in 1:length(dates)){
  this_date <- dates[i]
  pd <- df_country
  pd$value <- pd$deaths_non_cum
  pd <- pd %>%
    filter(date == max(this_date)) %>%
    dplyr::select(country, cases, cases_non_cum,
                  deaths, value) %>%
    dplyr::arrange(desc(value))
  pd <- pd[1:10,]
  pd <- pd %>% filter(value > 0)
  pd$country <- gsub(' ', '\n', pd$country)
  pd$country <- factor(pd$country, levels = pd$country)
  pd$color_var <- pd$country == 'Spain'
  if('Spain' %in% pd$country){
    cols <- rev(c('darkred', 'black'))
  } else {
    cols <- 'black'
  }
  g <- ggplot(data = pd,
         aes(x = country,
             y = value)) +
    geom_bar(stat = 'identity',
             aes(fill = color_var),
             alpha = 0.8,
             show.legend = FALSE) +
    theme_simple() +
    geom_text(aes(label = value),
              nudge_y = max(pd$value) * .05,
              size = 5,
              color = 'black') +
    labs(title = 'Daily (non-cumulative) COVID-19 deaths',
         subtitle = format(this_date, '%B %d')) +
    labs(x = 'Country',
         y = 'Deaths') +
    theme(axis.text = element_text(size = 15),
          plot.subtitle = element_text(size = 20)) +
    scale_fill_manual(name ='',
                      values = cols) +
    ylim(0, 900)
  ggsave(filename = paste0('/tmp/animation_deaths/', this_date, '.png'),
         g)
}
# Command line

cd /tmp/animation_deaths
mogrify -resize 50% *.png
convert -delay 50 -loop 0 *.png result.gif

Heatmap

pd <- by_country <-  esp_df %>% mutate(country = 'Spain') %>% 
  bind_rows(ita %>% mutate(country = 'Italy')) %>%
  bind_rows(por_df %>% mutate(country = 'Portugal')) %>%
  bind_rows(fra_df %>% mutate(country = 'France'))
pd$value <- pd$deaths_non_cum
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% ungroup %>% summarise(d = min(d)) %>% .$d
# pd$value <- ifelse(is.na(pd$value), 0, pd$value)
left <- expand.grid(date = seq(min(pd$date),
                               max(pd$date),
                               by = 1),
                    ccaa = sort(unique(pd$ccaa)))
right <- pd %>% dplyr::select(date, ccaa, value)
pd <- left_join(left, right) %>% mutate(value = ifelse(is.na(value), NA, value))
pd <- left_join(pd, by_country %>% dplyr::distinct(country, ccaa)) %>%
  filter(date <= max_date) %>%
  filter(value > 0)
the_limits <- c(1, 600)
the_breaks <- c(1, seq(100, 600, length = 6)) #seq(0, 600, length = 7)
pd$ccaa <- factor(pd$ccaa, levels = rev(unique(sort(pd$ccaa))))
ggplot(data = pd,
       aes(x = date,
           y = ccaa,
           color = value,
           size = value)) +
  # geom_tile(color = 'white') +
  geom_point(alpha = 0.8) +
  # geom_tile() +
  scale_color_gradientn(colors = rev(colorRampPalette(brewer.pal(n = 8, 'Spectral'))(5)),
                        name = '',
                        limits = the_limits,
                        breaks = the_breaks) +
    # scale_fill_gradientn(colors = rev(colorRampPalette(brewer.pal(n = 8, 'Spectral'))(5)),
    #                     name = '',
    #                     limits = the_limits,
    #                     breaks = the_breaks) +
  scale_size_area(name = '', limits = the_limits, breaks = the_breaks, max_size = 10) +
  
  theme_simple() +
  facet_wrap(~country, scales = 'free_y') +
  theme(strip.text = element_text(size = 20),
        axis.title = element_blank(),
        axis.text = element_text(size = 10),
        axis.text.x = element_text(size = 12)) +
  guides(color = guide_legend(), size = guide_legend()) +
  labs(title = 'Daily (non-cumulative) COVID-19 deaths by sub-state regions',
       caption = paste0('Data as of ', max_date))

ggsave('/tmp/1.png',
       width = 10,
       height = 8)

Heatmap per population

pd <- by_country <-  esp_df %>% mutate(country = 'Spain') %>%  bind_rows(ita %>% mutate(country = 'Italy'))
poppy <- bind_rows(ita_pop, esp_pop)
pd <- pd %>% left_join(poppy)
pd$value <- pd$deaths_non_cum / pd$pop * 1000000
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% ungroup %>% summarise(d = min(d)) %>% .$d
# pd$value <- ifelse(is.na(pd$value), 0, pd$value)
left <- expand.grid(date = seq(min(pd$date),
                               max(pd$date),
                               by = 1),
                    ccaa = sort(unique(pd$ccaa)))
right <- pd %>% dplyr::select(date, ccaa, value)
pd <- left_join(left, right) %>% mutate(value = ifelse(is.na(value), NA, value))
pd <- left_join(pd, by_country %>% dplyr::distinct(country, ccaa)) %>%
  filter(date <= max_date) %>%
  filter(value > 0)
the_limits <- c(1, 60)
the_breaks <- c(1, seq(10, 60, length = 6)) #seq(0, 600, length = 7)
pd$ccaa <- factor(pd$ccaa, levels = rev(unique(sort(pd$ccaa))))
ggplot(data = pd,
       aes(x = date,
           y = ccaa,
           color = value,
           size = value)) +
  # geom_tile(color = 'white') +
  geom_point(alpha = 0.8) +
  scale_color_gradientn(colors = rev(colorRampPalette(brewer.pal(n = 8, 'Spectral'))(5)),
                        name = '',
                        limits = the_limits,
                        breaks = the_breaks) +
  scale_size_area(name = '', limits = the_limits, breaks = the_breaks, max_size = 10) +
  theme_simple() +
  facet_wrap(~country, scales = 'free') +
  theme(strip.text = element_text(size = 26),
        axis.title = element_blank(),
        axis.text = element_text(size = 16),
        axis.text.x = element_text(size = 12)) +
  guides(color = guide_legend(), size = guide_legend()) +
  labs(title = 'Daily COVID-19 deaths per 1,000,000 population by sub-state regions',
       caption = paste0('Data as of ', max_date))

ggsave('/tmp/2.png',
       width = 10,
       height = 8)

Madrid vs rest of state

place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Otras CCAA')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Otras regiones italianas')
  # )
}
pd <- esp_df %>% mutate(country = 'España') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Italia')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = c('Madrid',
                             # 'Cataluña',
                             'Otras CCAA',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Otras regiones italianas'))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)
ggplot(data = pd,
       aes(x = date,
           y = deaths_non_cum_p,
           fill = ccaa,
           group = ccaa)) +
  geom_bar(stat = 'identity',
           position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  # geom_line(size = 0.2,
  #           aes(color = ccaa)) +
  xlim(as.Date('2020-03-09'),
       Sys.Date()-1) +
  theme_simple() +
  labs(x = 'Fecha',
       y = 'Muertes diarias por 1.000.000',
       title = 'Muertes por 1.000.000 habitantes') +
  theme(legend.position = 'top') +
  geom_text(aes(label = round(deaths_non_cum_p, digits = 1),
                color = ccaa,
                y = deaths_non_cum_p + 2,
                group = ccaa),
            size = 2.5,
            angle = 90,
            position = position_dodge(width = 0.99))

label_data <- pd %>%
  filter(country  %in% c('Italia', 'España')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -10))
ggplot(data = pd %>% group_by(country) %>% mutate(start_day = dplyr::first(date[deaths >=50])) %>% filter(date >= start_day),
       aes(x = date,
           y = p_deaths_country,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  geom_point(size = 3,
             aes(color = ccaa)) +
  facet_wrap(~country, scales = 'free_x') +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Fecha',
       y = 'Porcentaje de muertes',
       title = 'Porcentaje de muertes del país: región más afectada vs. resto del país',
       subtitle = 'A partir del primer día en cada país con 50 o más muertes') +
  theme(legend.position = 'top',
        strip.text = element_text(size = 30),
        legend.text = element_text(size = 10))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5)) +
  geom_text(data = label_data,
            aes(x = x - 2,
                y = y,
                label = label,
                color = ccaa),
            size = 6,
            show.legend = FALSE)

Italy regions, Spanish regions, Chinese regions (adjusted for population)

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# Chinese data
d <- df %>% filter(country == 'China') %>%
  mutate(cases = cases) %>%
  mutate(ccaa = district) %>%
  mutate(country = 'China') %>%
  left_join(chi_pop)
# join
joined <- bind_rows(a, b, d)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths_pm >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'Hubei (China)',
                          ifelse(country == 'Italy', 'Italia', 'España')))

lombardy_location <- (max(pd_big$days_since_start_deaths_pm[pd_big$ccaa == 'Lombardia']))

Error in eval(expr, envir, enclos): object 'pd_big' not found

# Define label data
label_data <- pd %>% group_by(ccaa) %>% filter(
                                                          (
                                                            (country == 'Hubei (China)' & days_since_start_deaths_pm == 22) |
                                                            (date == max(date) & country == 'España' & deaths_pm > 40 & days_since_start_deaths_pm >= 7) & ccaa != 'CyL' |
                                                              (date == max(date) & country == 'Italia' &
                                                                 ccaa != 'Liguria' & days_since_start_deaths_pm > 15)
                                                          ))
# Get differential label data based on what to be emphasized
bigs <- c('Madrid', 'Lombardia', 'Hubei')
label_data_big <- label_data %>%
  filter(ccaa %in% bigs)
label_data_small <- label_data %>%
  filter(!ccaa %in% bigs)

pd_big <- pd %>%
  filter(ccaa %in% bigs)
pd_small <- pd %>%
  filter(!ccaa %in% bigs)

# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- c('darkred', '#FF6633', '#006666')

ggplot(data = pd_big,
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small,
            aes(x = days_since_start_deaths_pm,
                y = deaths_pm,
                color = country),
            alpha = 0.7,
            size = 1) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c(cols)) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde "el comienzo del brote"',
       y = 'Muertes por millón de habitantes',
       title = 'Muertes por 1.000.000 habitantes',
       subtitle = paste0('Dia 0 ("comienzo del brote") = primer día a ', x_deaths_pm, ' o más muertes acumuladas por milión de población\nLíneas rojas: CCAA; líneas verde-azules: regiones italianas; línea naranja: Hubei, China'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data_big,
            aes(x = days_since_start_deaths_pm + 0.6,
                y = (deaths_pm + 50),
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths_pm + 0.6,
                y = deaths_pm  + (log(deaths_pm)/2),
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 16),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 25))  +
  xlim(0, lombardy_location + 5)

Error in limits(c(...), "x"): object 'lombardy_location' not found

Italy regions, Spanish regions, Chinese regions (raw numbers, not adjusted for population)

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# Chinese data
d <- df %>% filter(country == 'China') %>%
  mutate(cases = cases) %>%
  mutate(ccaa = district) %>%
  mutate(country = 'China') %>%
  left_join(chi_pop)
# join
joined <- bind_rows(a, b, d)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'China',
                          ifelse(country == 'Italy', 'Italia', 'España')))

# Define label data
label_data <- pd %>% group_by(ccaa) %>% filter(
                                                          (
                                                            (country == 'China' & deaths >10 & days_since_start_deaths == 29) |
                                                            (date == max(date) & country == 'España' & deaths > 90) |
                                                              (date == max(date) & country == 'Italia' &
                                                                 ccaa != 'Liguria' & days_since_start_deaths > 10)
                                                          ))
# Get differential label data based on what to be emphasized
label_data_big <- label_data %>%
  filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
label_data_small <- label_data %>%
  filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))

pd_big <- pd %>%
  filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
pd_small <- pd %>%
  filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))

# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- c( '#FF6633',  'darkred', '#006666')
ggplot(data = pd_big,
       aes(x = days_since_start_deaths,
           y = deaths,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small,
            aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
            alpha = 0.7,
            size = 1) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c(cols)) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde el primer día con 5 o más muertes acumuladas',
       y = 'Muertes',
       title = 'Muertes por COVID-19',
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data_big,
            aes(x = days_since_start_deaths + 1.6,
                y = ifelse(ccaa == 'Hubei', (deaths -500),
                           ifelse(ccaa == 'Lombardia',  (deaths + 700),
                                   (deaths + 300))),
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths + 1.6,
                align = 'left',
                y = deaths ,
                label = ccaa,
                # label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 30))  +
  xlim(0, 50)

ANIMATION: Italy regions, Spanish regions, Chinese regions (raw numbers, not adjusted for population)

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# Chinese data
d <- df %>% filter(country == 'China') %>%
  mutate(cases = cases) %>%
  mutate(ccaa = district) %>%
  mutate(country = 'China') %>%
  left_join(chi_pop)
# join
joined <- bind_rows(a, b, d)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'China',
                          ifelse(country == 'Italy', 'Italia', 'España')))


add_zero <- function(x, n){
  x <- as.character(x)
  adders <- n - nchar(x)
  adders <- ifelse(adders < 0, 0, adders)
  for (i in 1:length(x)){
    if(!is.na(x[i])){
      x[i] <- paste0(
        paste0(rep('0', adders[i]), collapse = ''),
        x[i],
        collapse = '')  
    } 
  }
  return(x)
}
# # Define label data
# label_data <- pd %>% group_by(ccaa) %>% filter(
#                                                           (
#                                                             (country == 'China' & deaths >10 & days_since_start_deaths == 29) |
#                                                             (date == max(date) & country == 'España' & deaths > 90) |
#                                                               (date == max(date) & country == 'Italia' &
#                                                                  ccaa != 'Liguria' & days_since_start_deaths > 10)
#                                                           ))
# # Get differential label data based on what to be emphasized
# label_data_big <- label_data %>%
#   filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
# label_data_small <- label_data %>%
#   filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
# 
pd_big <- pd %>%
  filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
pd_small <- pd %>%
  filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))



# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- c( '#FF6633',  'darkred', '#006666')

the_dir <- '/tmp/animation/'
dir.create(the_dir)
the_dates <- sort(unique(c(pd_big$date, pd_small$date)))
for(i in 1:length(the_dates)){
  
  the_date <- the_dates[i]
  pd_big_sub <- pd_big %>% filter(date <= the_date)
  pd_big_current <- pd_big_sub %>% filter(date == the_date)
  pd_small_sub <- pd_small %>% filter(date <= the_date)
  pd_small_current <- pd_small_sub %>% filter(date == the_date)

  label_data_big <-
    pd_big_sub %>%
    filter(ccaa %in% c('Lombardia', 'Madrid', 'Hubei')) %>%
    group_by(ccaa) %>%
    filter(date == max(date)) %>%
    ungroup %>%
    mutate(days_since_start_deaths = ifelse(ccaa == 'Hubei' &
                                              days_since_start_deaths >32,
                                            32,
                                            days_since_start_deaths))
  
  label_data_small <-
    pd_small_sub %>%
    filter(ccaa %in% c('Emilia Romagna',
                       'Cataluña',
                       'CLM',
                       'País Vasco',
                       'Veneto',
                       'Piemonte',
                       'Henan',
                       'Heilongjiang')) %>%
    group_by(ccaa) %>%
    filter(date == max(date))

  n_countries <- length(unique(pd_big_sub$country))
  if(n_countries == 3){
    sub_cols  <- cols
  }
  if(n_countries == 2){
    sub_cols <- cols[c(1,3)]
  }
   if(n_countries == 1){
    sub_cols <- cols[1]
  }
  g <- ggplot(data = pd_big_sub,
       aes(x = days_since_start_deaths,
           y = deaths,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small_sub,
            aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
            alpha = 0.7,
            size = 1) +
    geom_point(data = pd_big_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
               size = 3) +
    geom_point(data = pd_small_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
               size = 1, alpha = 0.6) +
    scale_y_log10(limits = c(5, 4500)) +
  scale_color_manual(name = '',
                     values = sub_cols) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde el primer día con 5 o más muertes acumuladas',
       y = 'Muertes',
       title = format(the_date, '%d %b'),
       subtitle = 'Muertes por COVID-19',
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data_big,
            aes(x = days_since_start_deaths + 1,
                y = deaths,
                hjust = 0,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths + 1.6,
                y = deaths ,
                label = ccaa,
                # label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 35),
        plot.subtitle = element_text(size = 24))  +
  xlim(0, 38) 
  message(i)
  ggsave(paste0(the_dir, add_zero(i, 3), '.png'),
         height = 7,
         width = 10.5)
}

# Command line
cd /tmp/animation
mogrify -resize 50% *.png
convert -delay 20 -loop 0 *.png result.gif

ANIMATION: Spain only

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
joined <- a
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'China',
                          ifelse(country == 'Italy', 'Italia', 'España')))

bigs <- c('Madrid', 'Cataluña', 'CLM', 'CyL', 'País Vasco', 'La Rioja')
pd_big <- pd %>%
  filter(ccaa %in% bigs)
pd_small <- pd %>%
  filter(!ccaa %in% bigs)



# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- colorRampPalette(c('#A16928','#bd925a','#d6bd8d','#edeac2', '#b5c8b8','#79a7ac','#2887a1'))(length(unique(pd$country)))

the_dir <- '/tmp/animation2/'
dir.create(the_dir)
the_dates <- sort(unique(c(pd_big$date, pd_small$date)))
for(i in 1:length(the_dates)){
  
  the_date <- the_dates[i]
  pd_big_sub <- pd_big %>% filter(date <= the_date)
  pd_big_current <- pd_big_sub %>% filter(date == the_date)
  pd_small_sub <- pd_small %>% filter(date <= the_date)
  pd_small_current <- pd_small_sub %>% filter(date == the_date)

  label_data_big <-
    pd_big_sub %>%
    filter(ccaa %in% bigs) %>%
    group_by(ccaa) %>%
    filter(date == max(date)) %>%
    ungroup
  
  label_data_small <-
    pd_small_sub %>%
    group_by(ccaa) %>%
    filter(date == max(date))
# sub_cols <- colorRampPalette(c('#A16928','#bd925a','#d6bd8d','#edeac2', '#b5c8b8','#79a7ac','#2887a1'))(length(unique(pd$ccaa)))
  sub_cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Dark2'))(length(unique(pd$ccaa)))
  # sub_cols <- rainbow((length(unique(pd$ccaa))))
  
  g <- ggplot(data = pd_big_sub,
       aes(x = days_since_start_deaths,
           y = deaths,
           group = ccaa)) +
  geom_line(aes(color = ccaa),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small_sub,
            aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
            alpha = 0.7,
            size = 1) +
    geom_point(data = pd_big_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
               size = 3) +
    geom_point(data = pd_small_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
               size = 1, alpha = 0.6) +
    geom_point(data = pd,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
               size = 1, alpha = 0.01) +
    scale_y_log10(limits = c(5, max(pd$deaths)*1.2),
                  breaks = c(10, 50, 100, 500, 1000)) +
  scale_color_manual(name = '',
                     values = sub_cols) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde el primer día con 5 o más muertes acumuladas',
       y = 'Muertes',
       title = format(the_date, '%d %b'),
       subtitle = 'Muertes por COVID-19',
       caption = '@joethebrew | www.databrew.cc')   +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 35),
        plot.subtitle = element_text(size = 24))  +
  xlim(0, 20) +
    theme(legend.position = 'none')
  message(i)
  if(nrow(label_data_big) > 0){
    g <- g +
      geom_text(data = label_data_big,
            aes(x = days_since_start_deaths + 0.2,
                y = deaths,
                hjust = 0,
                label = gsub(' ', ' ', ccaa),
                color = ccaa),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths + 0.2,
                y = deaths ,
                label = ccaa,
                # label = gsub(' ', '\n', ccaa),
                color = ccaa),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE)
  }
  
  ggsave(paste0(the_dir, add_zero(i, 3), '.png'),
         height = 7,
         width = 12)
}

# Command line
cd /tmp/animation
mogrify -resize 50% *.png
convert -delay 25 -loop 0 *.png result.gif

Italy regions for Spanish regions

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# join
joined <- bind_rows(a, b)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

ggplot(data = joined %>% filter(days_since_start_deaths_pm >= 0),
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.8,
            size = 2) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c('darkorange', 'purple')) +
  theme_simple() +
  theme(legend.position = 'none') +
  labs(x = 'Days since "start out outbreak"',
       y = 'Deaths per million',
       title = 'Deaths per capita, Italian regions vs. Spanish autonomous communities',
       subtitle = paste0('Day 0 ("start of outbreak") = first day at ', x_deaths_pm, ' or greater cumulative deaths per million'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = joined %>% group_by(ccaa) %>% filter(date == max(date) & 
                                                          (
                                                            (country == 'Spain' & deaths_pm > 25) |
                                                              (country == 'Italy' & days_since_start_deaths_pm > 10)
                                                          )),
            aes(x = days_since_start_deaths_pm + 0.6,
                y = deaths_pm,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 6) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20)) +
  xlim(0, 23)

ggsave('/tmp/italy_comparison.png',
       height = 6,
       width = 10)


# Separate for Catalonia
pd <- joined %>% filter(days_since_start_deaths_pm >= 0) %>%
         mutate(country = ifelse(ccaa == 'Cataluña',
                                 'Catalonia',
                                 country)) %>%
  mutate(ccaa = ifelse(ccaa == 'Cataluña', 'Catalunya', ccaa))
pdcat <- pd %>% filter(country == 'Catalonia')
label_data <- pd %>% group_by(ccaa) %>% filter(date == max(date) & 
                                                          (
                                                            (country == 'Catalonia') |
                                                            (country == 'Spain' & deaths_pm > 25) |
                                                              (country == 'Italy' & days_since_start_deaths_pm > 10)
                                                          ))
ggplot(data = pd,
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.3,
            size = 1.5) +
    geom_line(data = pdcat,
              aes(color = country),
            alpha = 0.8,
            size = 2) +
      geom_point(data = pdcat %>% filter(date == max(date)),
              aes(color = country),
            alpha = 0.8,
            size = 4) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c('darkred', 'darkorange', "purple")) +
  theme_simple() +
  theme(legend.position = 'none') +
  labs(x = 'Dies des del "començament del brot"',
       y = 'Morts per milió',
       title = 'Morts per càpita: Catalunya, comunitats autònomes, regions italianes',
       subtitle = paste0('Dia 0 ("començament del brot") = primer dia a ', x_deaths_pm, ' o més morts acumulades per milió de població'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data,
            aes(x = days_since_start_deaths_pm +0.2 ,
                y = deaths_pm +3,
                hjust = 0,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 6,
            alpha = 0.7) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20)) +
  xlim(0, 24)

ggsave('/tmp/cat_italy_comparison.png',
       height = 6,
       width = 10)


# Straightforward Lombardy, Madrid, Cat comparison
specials <- c('Lombardia', 'Madrid')
pd <- joined %>% filter(days_since_start_deaths_pm >= 0) %>%
         mutate(country = ifelse(ccaa == 'Cataluña',
                                 'Catalonia',
                                 country)) %>%
  mutate(ccaa = ifelse(ccaa == 'Cataluña', 'Catalunya', ccaa))
pdcat <- pd %>% filter(ccaa %in%  specials)
label_data <- pd %>% group_by(ccaa) %>% filter(date == max(date) & 
                                                          (
                                                            # (country == 'Catalonia') |
                                                            (country == 'Spain' & deaths_pm > 20) |
                                                              (country == 'Italy' & days_since_start_deaths_pm >= 10)
                                                          ))
ggplot(data = pd,
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.3,
            size = 1.5) +
    geom_line(data = pdcat,
              aes(color = country),
            alpha = 0.8,
            size = 2) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c('darkred', 'darkorange', "purple")) +
  theme_simple() +
  theme(legend.position = 'none') +
  labs(x = 'Dias desde "el comienzo del brote"',
       y = 'Muertes por millón de habitantes',
       title = 'Muertes acumuladas por 1.000.000 habitantes',
       subtitle = paste0('Dia 0 ("comienzo del brote") = primer día a ', x_deaths_pm, ' o más muertes acumuladas por milión de población'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data %>% filter(!ccaa %in% specials),
            aes(x = days_since_start_deaths_pm + 0.4,
                y = deaths_pm +3,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.5) +
    geom_text(data = label_data %>% filter(ccaa %in% specials),
            aes(x = days_since_start_deaths_pm ,
                y = deaths_pm +30,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.8) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20)) +
  xlim(0, 23)

ggsave('/tmp/mad_lom_italy_comparison.png',
       height = 6,
       width = 10)

Loop for regions of the world

isos <- sort(unique(world_pop$sub_region))
isos <- c('Central Asia', 'Eastern Asia', 'Eastern Europe',
          'Latin America and the Caribbean',
          'Northern Africa', 'Northern America',
          'Nothern Europe',
          'South-eastern Asia',
          'Southern Asia', 'Southern Europe',
          'Sub-Saharan Africa', 'Western Asia', 'Western Europe')
dir.create('/tmp/world')
for(i in 1:length(isos)){
  this_iso <- isos[i]
  message(i, ' ', this_iso)
  countries <- world_pop %>% filter(sub_region == this_iso)
  pd <- df %>%
    filter(!country %in% c('Guyan
                           a', 'Bahamas', 'The Bahamas')) %>%
          group_by(country, iso, date) %>%
          summarise(cases = sum(cases, na.rm = TRUE)) %>%
    ungroup %>%
    group_by(country) %>%
         filter(length(which(cases > 0)) > 1) %>%
    ungroup %>%
         filter(iso %in% countries$iso)
  if(nrow(pd) > 0){
    cols <- colorRampPalette(brewer.pal(n = 8, 'Spectral'))(length(unique(pd$country)))
cols <- sample(cols, length(cols))
    # Plot
n_countries <- (length(unique(pd$country)))
ggplot(data = pd,
       aes(x = date,
           # color = country,
           # fill = country,
           y = cases)) +
  theme_simple() +
  # geom_point() +
  # geom_line() +
  geom_area(fill = 'darkred', alpha = 0.3, color = 'darkred') +
  # scale_color_manual(name = '',
  #                    values = cols) +
  # scale_fill_manual(name = '',
  #                   values = cols) +
  theme(legend.position = 'none',
        axis.text = element_text(size = 6),
        strip.text = element_text(size = ifelse(n_countries > 20, 6,
                                                ifelse(n_countries > 10, 10,
                                                       ifelse(n_countries > 5, 11, 12))) ),
        legend.text = element_text(size = 6)) +
  # scale_y_log10() +
  facet_wrap(~country,
             scales = 'free') +
  labs(x = '',
       y = 'Confirmed cases',
       title = paste0('Confirmed cases of COVID-19 in ', this_iso)) 
  ggsave(paste0('/tmp/world/', this_iso, '.png'),
         width = 12, 
         height = 7)
  }



}

Rolling average new events

plot_data <- df_country %>% filter(country %in% c('Spain', 'France', 'Italy', 'Germany', 'Belgium', 'Norway')) %>% mutate(geo = country)
roll_curve(plot_data, pop = T)

dir.create('/tmp/countries')
roll_curve_country <- function(the_country = 'Spain'){
  plot_data <- df_country %>% filter(country %in% the_country) %>% mutate(geo = country)
  g1 <- roll_curve(plot_data, pop = F)
  g2 <- roll_curve(plot_data, pop = T)
  g3 <- roll_curve(plot_data, pop = F, deaths = T)
  g4 <- roll_curve(plot_data, pop = T, deaths = T)
  ggsave(paste0('/tmp/countries/', the_country, '1.png'), g1)
  ggsave(paste0('/tmp/countries/', the_country, '2.png'), g2)
  ggsave(paste0('/tmp/countries/', the_country, '3.png'), g3)
  ggsave(paste0('/tmp/countries/', the_country, '4.png'), g4)
}


countries <- c('Spain', 'France', 'Italy', 'Germany', 'Belgium', 'Norway', 'US', 'United Kingdom', 'Korea, South',
  'China', 'Japan', 'Switzerland', 'Sweden', 'Denmark', 'Netherlands', 'Iran', 'Canada')
for(i in 1:length(countries)){
  roll_curve_country(the_country = countries[i])
}

Error in ts(x): 'ts' object must have one or more observations

# Cases
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data)

# Cases adjusted
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data, pop = T)

# Deaths
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data, deaths = T)

# Cases adjusted
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data, pop = T, deaths = T)

plot_data <- esp_df  %>% mutate(geo = ccaa)

roll_curve(plot_data, pop = T, deaths = T)

plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)

roll_curve(plot_data, deaths = T)

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths / sum(deaths) * 100)
text_size <- 12

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = deaths)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths | Muertes',
       title = 'COVID-19 deaths in Spain',
       subtitle = paste0('Data as of ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(deaths > 0),
            aes(x = ccaa,
                y = deaths,
                label = paste0(deaths, '\n(',
                               round(p, digits = 1), '%)')),
            size = text_size * 0.3,
            nudge_y = 180) +
  ylim(0, max(pd$deaths * 1.1))

ggsave('/tmp/spain.png')

Muertes relativas por CCAA

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths / sum(deaths) * 100)

pd <- pd %>% left_join(esp_pop)
text_size <- 12
pd$value <- pd$deaths / pd$pop * 100000

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths per 100,000',
       title = 'COVID-19 deaths per 100.000',
       subtitle = paste0('Data as of ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(value > 0),
            aes(x = ccaa,
                y = value,
                label = paste0(round(value, digits = 2), '\n(',
                               deaths, '\ndeaths)')),
            size = text_size * 0.3,
            nudge_y = 4.5) +
  ylim(0, max(pd$value) * 1.2)

ggsave('/tmp/spai2.png')

Just yesterday

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths_non_cum / sum(deaths_non_cum) * 100)
text_size <- 12

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = deaths_non_cum)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths',
       title = 'COVID-19 deaths in Spain',
       subtitle = paste0('Data only for ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(deaths_non_cum > 0),
            aes(x = ccaa,
                y = deaths_non_cum,
                label = paste0(deaths_non_cum, '\n(',
                               round(p, digits = 1), '%)')),
            size = text_size * 0.3,
            nudge_y = 30) +
  ylim(0, max(pd$deaths_non_cum * 1.1))

ggsave('/tmp/spain_non_cum.png')

Muertes relativas por CCAA ayer SOLO

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths_non_cum / sum(deaths_non_cum) * 100)

pd <- pd %>% left_join(esp_pop)
text_size <- 12
pd$value <- pd$deaths_non_cum / pd$pop * 100000

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths per 100,000',
       title = 'COVID-19 deaths per 100.000',
       subtitle = paste0('Data as of ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(value > 0),
            aes(x = ccaa,
                y = value,
                label = paste0(round(value, digits = 2), '\n(',
                               deaths_non_cum, '\ndeaths)')),
            size = text_size * 0.3,
            nudge_y = 1) +
  ylim(0, max(pd$value) * 1.3)

ggsave('/tmp/spain_ayer_adj.png')

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, scales = 'fixed')

ggsave('/tmp/a.png',
       width = 1280 / 150,
       height = 720 / 150)

Loop for everywhere (see desktop)

dir.create('/tmp/ccaas')
ccaas <- sort(unique(esp_df$ccaa))
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, scales = 'fixed')  + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_cases.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

ccaas <- sort(unique(esp_df$ccaa))
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, scales = 'fixed', pop = TRUE)  + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_cases_pop.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

# Deaths too
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, deaths = T, scales = 'fixed') + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_deaths.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

# Deaths too
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, deaths = T, scales = 'fixed', pop = TRUE)  + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_deaths_pop.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, scales = 'free_y')

ggsave('/tmp/b.png',
       width = 1280 / 150,
       height = 720 / 150)

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, deaths = T, scales = 'free_y')

ggsave('/tmp/c.png',
       width = 1280 / 150,
       height = 720 / 150)

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, deaths = T, scales = 'fixed')

ggsave('/tmp/d.png',
       width = 1280 / 150,
       height = 720 / 150)

plot_data <- df_country %>% filter(country %in% c('Spain', 'Italy', 'Germany', 'France', 'US',
                                                  'China', 'Korea, South', 'Japan', 'Singapore')) %>% mutate(geo = country)
roll_curve(plot_data, scales = 'free_y')

ggsave('/tmp/z.png',
       width = 1280 / 150,
       height = 720 / 150)

World at large

pd <- df_country %>%
  group_by(date) %>%
  summarise(n = sum(cases)) %>%
  filter(date < max(date))
ggplot(data = pd,
       aes(x = date,
           y = n)) +
  geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Cases',
       title = 'COVID-19 cases')

ggsave('~/Videos/update/a.png',
       width = 1280 / 150,
       height = 720 / 150)

Error in grid.newpage(): could not open file '/home/joebrew/Videos/update/a.png'

China vs world

pd <- df_country %>%
  group_by(date,
           country = ifelse(country == 'China', 'China', 'Other countries')) %>%
  summarise(n = sum(cases))  %>%
  ungroup %>%
  filter(date < max(date))

Error: Column `country` can't be modified because it's a grouping variable

ggplot(data = pd,
       aes(x = date,
           y = n,
           color = country)) +
  geom_line(size = 2) +
  # geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Cases',
       title = 'COVID-19 cases') +
  scale_color_manual(name = '',
                     values = c('red', 'black')) +
  theme(legend.text = element_text(size = 25),
        legend.position = 'top')

Error in FUN(X[[i]], ...): object 'country' not found

ggsave('~/Videos/update/b.png',
       width = 1280 / 150,
       height = 720 / 150)

Error in grid.newpage(): could not open file '/home/joebrew/Videos/update/b.png'

NOn china only

pd <- df_country %>%
  group_by(date,
           country = ifelse(country == 'China', 'China', 'Other countries')) %>%
  summarise(n = sum(cases)) %>%
  filter(country == 'Other countries')  %>%
  ungroup %>%
  filter(date < max(date))

Error: Column `country` can't be modified because it's a grouping variable

ggplot(data = pd,
       aes(x = date,
           y = n)) +
  geom_line(size = 2) +
  # geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Cases',
       title = 'COVID-19 cases, outside of China') 

ggsave('~/Videos/update/c.png',
       width = 1280 / 150,
       height = 720 / 150)

Error in grid.newpage(): could not open file '/home/joebrew/Videos/update/c.png'

Case numbers across countries

plot_day_zero(countries = c('France', 'Germany', 'Italy', 'Spain', 'Switzerland', 'Sweden', 'Norway', 'Netherlands'))

# ggsave('~/Videos/update/d.png',
#        width = 1280 / 150,
#        height = 720 / 150)

World at large - deaths

pd <- df_country %>%
  group_by(date) %>%
  summarise(n = sum(deaths)) %>%
  filter(date < max(date))
ggplot(data = pd,
       aes(x = date,
           y = n)) +
  geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Deaths',
       title = 'COVID-19 deaths')

# ggsave('~/Videos/update/e.png',
#        width = 1280 / 150,
#        height = 720 / 150)

China vs world deaths

pd <- df_country %>%
  group_by(date,
           country = ifelse(country == 'China', 'China', 'Other countries')) %>%
  summarise(n = sum(deaths))  %>%
  ungroup %>%
  filter(date < max(date))

Error: Column `country` can't be modified because it's a grouping variable

ggplot(data = pd,
       aes(x = date,
           y = n,
           color = country)) +
  geom_line(size = 2) +
  # geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Deaths',
       title = 'COVID-19 deaths') +
  scale_color_manual(name = '',
                     values = c('red', 'black')) +
  theme(legend.text = element_text(size = 25),
        legend.position = 'top')

Error in FUN(X[[i]], ...): object 'country' not found

# ggsave('~/Videos/update/f.png',
#        width = 1280 / 150,
#        height = 720 / 150)

Asian hope

plot_day_zero(countries = c('Korea, South', 'Japan', 'China', 'Singapore'))

# ggsave('~/Videos/update/g.png',
#        width = 1280 / 150,
#        height = 720 / 150)

Since trajectories are very unstable when cases are low, we’ll exclude from our analysis the first few days, and will only count as “outbreak” once a country reaches 150 or more cumulative cases.

# Doubling time
n_cases_start = 150
countries = c('Italy', 'Spain', 'France', 'Germany', 'Italy', 'Switzerland', 'Denmark', 'US', 'United Kingdom', 'Norway')
# countries <- sort(unique(df_country$country))
out_list <- curve_list <-  list()
counter <- 0
for(i in 1:length(countries)){
  message(i)
  this_country <- countries[i]
  sub_data <-df_country %>% filter(country == this_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  ok <- max(sub_data$cases, na.rm = TRUE) >= n_cases_start
  if(ok){
    counter <- counter + 1
    pd <- sub_data %>%
      filter(!is.na(cases)) %>%
      mutate(start_date = min(date[cases >= n_cases_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since))
    fit <- lm(log(cases) ~ days_since, data = pd) 
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    curve <- fit$coef[2]
    
    # Predict days ahead
    fake <- tibble(days_since = seq(0, max(pd$days_since) + 5, by = 1))
    fake <- left_join(fake, pd %>% dplyr::select(days_since, cases, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    
    # Doubling time
    dt <- log(2)/fit$coef[2]
    out <- tibble(country = this_country,
                  doubling_time = dt,
                  slope = curve)
    out_list[[counter]] <- out
    curve_list[[counter]] <- fake %>% mutate(country = this_country)
  }
}
done <- bind_rows(out_list)
print(done)

# A tibble: 10 x 3
   country        doubling_time  slope
   <chr>                  <dbl>  <dbl>
 1 Italy                   8.48 0.0817
 2 Spain                   6.91 0.100 
 3 France                  7.23 0.0959
 4 Germany                 7.44 0.0932
 5 Italy                   8.48 0.0817
 6 Switzerland            10.2  0.0682
 7 Denmark                13.0  0.0532
 8 US                      5.22 0.133 
 9 United Kingdom          6.12 0.113 
10 Norway                 15.1  0.0460

curves <- bind_rows(curve_list)
# Get curves back in exponential form
# curves$curve <- exp(curves$curve)

# Join doubling time to curves
joined <- left_join(curves, done)

# Get rid of Italy future (since it's the "leader")
joined <- joined %>%
  filter(country != 'Italy' |
           date <= (Sys.Date() -1))


# Make long format
long <- joined %>% 
  dplyr::select(date, days_since, country, cases, predicted, doubling_time) %>%
  tidyr::gather(key, value, cases:predicted) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

The below chart shows the trajectories in terms of number of cases in Europe in red, and the predicted trajectories in black. The black line assumes that the doubling rate will stay constant.

cols <- c('red', 'black')
ggplot(data = long,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = long %>% filter(key != 'Confirmed cases'),
            size = 1.2, alpha = 0.8) +
  geom_point(data = long %>% filter(key == 'Confirmed cases')) +
  geom_line(data = long %>% filter(key == 'Confirmed cases'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >150 cumulative cases',
       y = 'Cases',
       title = 'COVID-19 CASES: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >150 cumulative cases)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15))

Since Italy is “leading the way”, it’s helpful to also compare each country to Italy. Let’s see that.

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Italy')
ol2 <- joined %>% filter(country == 'Italy') %>% dplyr::rename(Italy = cases) %>%
  dplyr::select(Italy, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, cases, predicted, Italy,doubling_time)
ol <- tidyr::gather(ol, key, value, cases: Italy) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

cols <- c('red', 'blue', 'black')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = ol %>% filter(!key %in% c('Confirmed cases', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Italy')),
            size = 0.8, alpha = 0.8) +
  geom_point(data = ol %>% filter(key == 'Confirmed cases')) +
  geom_line(data = ol %>% filter(key == 'Confirmed cases'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >150 cumulative cases',
       y = 'Cases',
       title = 'COVID-19 CASES: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >150 cumulative cases)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15))

In the above, what’s striking is how many places have trajectories that are worse than Italy’s. Yes, Italy has more cases, but it’s doubling time is less. Either that changes soon, or these other countries will soon have more cases than Italy.

Deaths or cases?

The number of cases is not necessarily the best indicator for the severity of an outbreak of this nature. Why? Because (a) testing rates and protocols are different by place and (b) testing rates are different by time (since health services are changing their approaches as things develop). In other words, when we compare the number of cases by place and time, we are introducing significant bias.

Using deaths to gauge the magnitude of the outbreak is also problematic. Death rates are differential by age, so the number of deaths depends on a country’s population period, or age structure. Also, death rates will be a function of health services, which are not of the same quality every where. And, of course, like cases, we don’t necessarily know about all of the deaths that occur because of COVID-19.

Still, there’s an argument that death rates have less bias than case rates because deaths are easier to identify than cases. Let’s accept that argument, for the time being, and have a look at death rates by country.

# Doubling time
n_deaths_start = 5
countries = c('Italy', 'Spain', 'France', 'Italy', 'Switzerland', 'Denmark', 'US', 'United Kingdom', 'Norway', 'Germany')
# countries <- sort(unique(df_country$country))

make_double_time <- function(data = df_country,
                             the_country = 'Spain',
                             n_deaths_start = 5,
                             time_ahead = 7){
   sub_data <-data %>% filter(country == the_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  ok <- max(sub_data$deaths, na.rm = TRUE) >= n_deaths_start
  if(ok){
    counter <- counter + 1
    pd <- sub_data %>%
      filter(!is.na(deaths)) %>%
      mutate(start_date = min(date[deaths >= n_deaths_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since)) %>%
      mutate(the_weight = 1/(1 + (as.numeric(max(date) - date))))
    fit <- lm(log(deaths) ~ days_since,
              weights = the_weight,
              data = pd) 
    # fitlo <- loess(deaths ~ days_since, data = pd)
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    # curve <- fit$coef[2]
    
    # Predict days ahead
    day0 <- pd$date[pd$days_since == 0]
    fake <- tibble(days_since = seq(0, max(pd$days_since) + time_ahead, by = 1))
    fake <- fake %>%mutate(date = seq(day0, day0+max(fake$days_since), by = 1))
    fake <- left_join(fake, pd %>% dplyr::select(days_since, deaths, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    # fake$predictedlo <- predict(fitlo, newdata = fake)
    ci <- exp(predict(fit, newdata = fake, interval = 'prediction'))
    # cilo <- predict(fitlo, newdata = fake, interval = 'prediction')

    fake$lwr <- ci[,'lwr']
    fake$upr <- ci[,'upr']
    # fake$lwrlo <- ci[,'lwr']
    # fake$uprlo <- ci[,'upr']
    # Doubling time
    dt <- log(2)/fit$coef[2]
    fake %>% mutate(country = the_country) %>% mutate(doubling_time = dt)
  }
}

plot_double_time <- function(data, ylog = F){
  the_labs <- labs(x = 'Date',
                   y = 'Deaths',
                   title = paste0('Predicted deaths in ', data$country[1]))
  long <- data %>%
    tidyr::gather(key, value, deaths:predicted) %>%
    mutate(key = Hmisc::capitalize(key))
  g <- ggplot() +
        geom_ribbon(data = data %>% filter(date > max(long$date[!is.na(long$value) & long$key == 'Deaths'])),
                aes(x = date,
                    ymax = upr,
                    ymin = lwr),
                alpha =0.6,
                fill = 'darkorange') +
    geom_line(data = long,
              aes(x = date,
                  y = value,
                  group = key,
                  lty = key)) +
    geom_point(data = long %>% filter(key == 'Deaths'),
               aes(x = date,
                   y = value)) +
    theme_simple() +
    theme(legend.position = 'right',
          legend.title = element_blank()) +
    the_labs
  if(ylog){
    g <- g + scale_y_log10()
  }
  return(g)
}
options(scipen = '999')
data <- make_double_time(n_deaths_start = 150, time_ahead = 7)
data

# A tibble: 61 x 8
   days_since date       country deaths predicted   lwr   upr doubling_time
        <dbl> <date>     <chr>    <dbl>     <dbl> <dbl> <dbl>         <dbl>
 1          0 2020-03-14 Spain      292     2519. 1656. 3833.          14.5
 2          1 2020-03-15 Spain      314     2642. 1747. 3996.          14.5
 3          2 2020-03-16 Spain      496     2772. 1844. 4165.          14.5
 4          3 2020-03-17 Spain      590     2907. 1946. 4342.          14.5
 5          4 2020-03-18 Spain      765     3049. 2054. 4527.          14.5
 6          5 2020-03-19 Spain      993     3198. 2168. 4720.          14.5
 7          6 2020-03-20 Spain     1326     3355. 2287. 4921.          14.5
 8          7 2020-03-21 Spain     1672     3519. 2413. 5131.          14.5
 9          8 2020-03-22 Spain     2136     3691. 2546. 5351.          14.5
10          9 2020-03-23 Spain     2707     3871. 2686. 5581.          14.5
# … with 51 more rows

dir.create('/tmp/ccaa_predictions')

plot_double_time(data, ylog = T) +
  labs(subtitle = 'Basic log-linear model weighted at (1 + (1/ days ago)),\nassuming no change in growth trajectory since first day at >150 deaths')

ggsave('/tmp/ccaa_predictions/spain.png')
# All ccaas
ccaas <- sort(unique(esp_df$ccaa))
for(i in 1:length(ccaas)){
  message(i)
  this_ccaa <- ccaas[i]
  sub_data <- esp_df %>% mutate(country = ccaa) 
  try({
    data <- make_double_time(
    data = sub_data,
    the_country = this_ccaa,
    n_deaths_start = 5,
    time_ahead = 7)
  plot_double_time(data, ylog = T) +
  labs(subtitle = 'Basic log-linear model weighted at (1 + (1/ days ago)), assuming no change in growth trajectory since first day at >5 deaths')
  ggsave(paste0('/tmp/ccaa_predictions/',
                this_ccaa, '.png'),
         height = 4.9,
         width = 8.5)
  })

}

Error in UseMethod("gather_") : 
  no applicable method for 'gather_' applied to an object of class "NULL"

Error in UseMethod("gather_") : 
  no applicable method for 'gather_' applied to an object of class "NULL"

# all_countries <- sort(unique(df_country$country))
# for(i in 1:length(all_countries)){
#   this_country <- all_countries[i]
#   data <- make_double_time(the_country = this_country, n_deaths_start = 5)
#   if(!is.null(data)){
#     # print(this_country)
#     g <- plot_double_time(data, ylog = F) +
#   labs(subtitle = 'Basic log-linear model assuming no change in growth trajectory since first day at >5 deaths')
#     ggsave(paste0('/tmp/', this_country, '.png'), height = 5, width = 8)
#     print(data)
#   }
# }

counter <- 0
# Africa
data <- make_double_time(the_country = 'South Africa',
                         n_deaths_start = 5, time_ahead = 7)
data

# A tibble: 44 x 8
   days_since date       country      deaths predicted   lwr   upr doubling_time
        <dbl> <date>     <chr>         <dbl>     <dbl> <dbl> <dbl>         <dbl>
 1          0 2020-03-31 South Africa      5      9.05  7.39  11.1          8.54
 2          1 2020-04-01 South Africa      5      9.81  8.05  12.0          8.54
 3          2 2020-04-02 South Africa      5     10.6   8.77  12.9          8.54
 4          3 2020-04-03 South Africa      9     11.5   9.55  13.9          8.54
 5          4 2020-04-04 South Africa      9     12.5  10.4   15.1          8.54
 6          5 2020-04-05 South Africa     11     13.6  11.3   16.3          8.54
 7          6 2020-04-06 South Africa     12     14.7  12.3   17.6          8.54
 8          7 2020-04-07 South Africa     13     16.0  13.4   19.0          8.54
 9          8 2020-04-08 South Africa     18     17.3  14.6   20.5          8.54
10          9 2020-04-09 South Africa     18     18.8  15.9   22.1          8.54
# … with 34 more rows

dir.create('/tmp/africa_predictions')

plot_double_time(data, ylog = T) +
  labs(subtitle = 'Basic log-linear model weighted at (1 + (1/ days ago)),\nassuming no change in growth trajectory since first day at >150 deaths')

out_list <- curve_list <-  list()
counter <- 0
for(i in 1:length(countries)){
  message(i)
  this_country <- countries[i]
  sub_data <-df_country %>% filter(country == this_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  ok <- max(sub_data$deaths, na.rm = TRUE) >= n_deaths_start
  if(ok){
    counter <- counter + 1
    pd <- sub_data %>%
      filter(!is.na(deaths)) %>%
      mutate(start_date = min(date[deaths >= n_deaths_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since))
    fit <- lm(log(deaths) ~ days_since, data = pd) 
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    # curve <- fit$coef[2]
    
    # Predict days ahead
    fake <- tibble(days_since = seq(0, max(pd$days_since) + 5, by = 1))
    fake <- left_join(fake, pd %>% dplyr::select(days_since, deaths, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    
    # Doubling time
    dt <- log(2)/fit$coef[2]
    out <- tibble(country = this_country,
                  doubling_time = dt)
    out_list[[counter]] <- out
    curve_list[[counter]] <- fake %>% mutate(country = this_country)
  }
}
done <- bind_rows(out_list)
curves <- bind_rows(curve_list)
# Get curves back in exponential form
# curves$curve <- exp(curves$curve)

# Join doubling time to curves
joined <- left_join(curves, done)

# Get rid of Italy future (since it's the "leader")
joined <- joined %>%
  filter(country != 'Italy' |
           date <= (Sys.Date() -1))


# Make long format
long <- joined %>% 
  dplyr::select(date, days_since, country, deaths, predicted, doubling_time) %>%
  tidyr::gather(key, value, deaths:predicted) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

cols <- c('red', 'black')
sub_data <-  long %>% filter(country != 'US')
ggplot(data = sub_data,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = sub_data %>% filter(key != 'Deaths'),
            size = 1.2, alpha = 0.8) +
  geom_point(data = sub_data %>% filter(key == 'Deaths')) +
  geom_line(data = sub_data %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 cumulative deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15))

Let’s again overlay Italy.

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Italy')
ol2 <- joined %>% filter(country == 'Italy') %>% dplyr::rename(Italy = deaths) %>%
  dplyr::select(Italy, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Italy,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Italy) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

cols <- c('red', 'blue', 'black')
sub_data <- ol %>% 
  filter(!(key == 'Predicted (based on current doubling time)' &
             country == 'Spain' &
             days_since > 13))
ggplot(data = sub_data,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = sub_data %>% filter(!key %in% c('Deaths', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = sub_data %>% filter(key %in% c('Italy')),
            size = 0.8, alpha = 0.8) +
  geom_point(data = sub_data %>% filter(key == 'Deaths')) +
  geom_line(data = sub_data %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  scale_y_log10() +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15)) 

Let’s look just at Spain

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Italy',
                         country == 'Spain')
ol2 <- joined %>% filter(country == 'Italy') %>% dplyr::rename(Italy = deaths) %>%
  dplyr::select(Italy, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Italy,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Italy) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', 
                      ifelse(key == 'Deaths', 'Spain', key)))

cols <- c('blue',  'black', 'red')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Italy')),
            size = 0.8, alpha = 0.8) +
  # geom_point(data = ol %>% filter(key == 'Deaths')) +
    geom_point(data = ol %>% filter(country == 'Spain',
                                    key == 'Spain'), size = 4, alpha = 0.6) +

  geom_line(data = ol %>% filter(key == 'Deaths'),
            size = 0.8) +
  # facet_wrap(~paste0(country, '\n',
  #                    '(doubling time: ', 
  #                    round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,1)) +
  scale_color_manual(name = '',
                     values = cols) +
  scale_y_log10() +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15),
          axis.title = element_text(size = 18))

The importance of lag

Things are changing very rapidly. And measures being taken by these countries will have an impact on the outbreak.

But it’s important to remember that there is a lag between when an intervention takes place and when its effect is notable. Because of the incubation period - the number of days between someone getting infected and becoming sick - what we do today won’t really have an effect until next weekend. And the clinical cases that present today are among people who got infected a week ago.

Disease control measures work. We can see that clearly in the case of Hubei, Wuhan, Iran, Japan. And they will work in Europe too. But because many of these measures were implemented very recently, we won’t likely see a major effect for at least a few more days.

In the mean time, it’s important to practice social distancing. Stay away from others to keep both you and others safe. Listen to Health Authorities. Take this very seriously.

Spain and Italy regions

# Madrid vs Lombardy deaths
n_death_start <- 5
pd <- esp_df %>%
  # filter(ccaa == 'Madrid') %>%
  dplyr::select(date, ccaa, cases, deaths) %>%
  bind_rows(ita %>%
              # filter(ccaa == 'Lombardia') %>%
              dplyr::select(date, ccaa, cases, deaths)) %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(first_n_death = min(date[deaths >= n_death_start])) %>%
  ungroup %>%
  mutate(days_since_n_deaths = date - first_n_death) %>%
  filter(is.finite(days_since_n_deaths))

pd$country <- pd$ccaa
pd$cases <- pd$cases
countries <- sort(unique(pd$country))
out_list <- curve_list <-  list()
counter <- 0
for(i in 1:length(countries)){
  message(i)
  this_country <- countries[i]
  sub_data <- pd %>% filter(country == this_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  # ok <- max(sub_data$deaths, na.rm = TRUE) >= n_deaths_start
  ok <- length(which(sub_data$deaths >= n_deaths_start))
  if(ok){
    counter <- counter + 1
    sub_pd <- sub_data %>%
      filter(!is.na(deaths)) %>%
      mutate(start_date = min(date[deaths >= n_deaths_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since))
    fit <- lm(log(deaths) ~ days_since, data = sub_pd) 
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    # curve <- fit$coef[2]
    
    # Predict days ahead
    fake <- tibble(days_since = seq(0, max(sub_pd$days_since) + 5, by = 1))
    fake <- left_join(fake, sub_pd %>% dplyr::select(days_since, deaths, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    
    # Doubling time
    dt <- log(2)/fit$coef[2]
    out <- tibble(country = this_country,
                  doubling_time = dt)
    out_list[[counter]] <- out
    curve_list[[counter]] <- fake %>% mutate(country = this_country)
  }
}
done <- bind_rows(out_list)
curves <- bind_rows(curve_list)
# Get curves back in exponential form
# curves$curve <- exp(curves$curve)

# Join doubling time to curves
joined <- left_join(curves, done)


# Make long format
long <- joined %>% 
  dplyr::select(date, days_since, country, deaths, predicted, doubling_time) %>%
  tidyr::gather(key, value, deaths:predicted) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

# Remove those with not enough data to have a doubling time yet
long <- long %>% filter(!is.na(doubling_time))

text_size <- 12

cols <- c('red', 'black')
ggplot(data = long,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = long %>% filter(key != 'Deaths'),
            size = 1.2, alpha = 0.8) +
  geom_point(data = long %>% filter(key == 'Deaths')) +
  geom_line(data = long %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_y_log10() +
  scale_linetype_manual(name ='',
                        values = c(1,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >150 cumulative cases',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = text_size * 0.5),
          plot.title = element_text(size = 15))

Let’s overlay Lombardy

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Lombardia')
ol2 <- joined %>% filter(country == 'Lombardia') %>% dplyr::rename(Lombardia = deaths) %>%
  dplyr::select(Lombardia, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Lombardia,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Lombardia) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

# Remove those with not enough data to have a doubling time yet
ol <- ol %>% filter(!is.na(doubling_time))

cols <- c('red', 'blue', 'black')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  scale_y_log10() +
  geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Lombardia')),
            size = 0.5, alpha = 0.8) +
  geom_point(data = ol %>% filter(key == 'Deaths')) +
  geom_line(data = ol %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = text_size * 0.5),
          plot.title = element_text(size = 15))

Show only Spanish regions vs. Lombardy

text_size <- 14

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Lombardia')
ol2 <- joined %>% filter(country == 'Lombardia') %>% dplyr::rename(Lombardia = deaths) %>%
  dplyr::select(Lombardia, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Lombardia,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Lombardia) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

# Remove those with not enough data to have a doubling time yet
ol <- ol %>% filter(!is.na(doubling_time))

# Only Spain
ol <- ol %>% filter(country %in% esp_df$ccaa) %>%
  filter(!country %in% 'Aragón')

cols <- c('red', 'blue', 'black')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  scale_y_log10() +
  geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Lombardia')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Lombardia')),
            size = 0.5, alpha = 0.8) +
  geom_point(data = ol %>% filter(key == 'Deaths')) +
  geom_line(data = ol %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = text_size * 0.6),
          plot.title = element_text(size = 15))

Same plot but overlayed

Same as above, but overlaid

text_size <-10

# cols <- c('red', 'black')
long <- long %>% filter(country %in% c('Lombardia',
                                       'Emilia Romagna') |
                          country %in% esp_df$ccaa) %>%
  filter(country != 'Aragón')
places <- sort(unique(long$country))

cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 7, 'Spectral'))(length(places))
cols[which(places == 'Madrid')] <- 'red'
cols[which(places == 'Cataluña')] <- 'purple'
cols[which(places == 'Lombardia')] <- 'darkorange'
cols[which(places == 'Emilia Romagna')] <- 'darkgreen'

long$key <- ifelse(long$key != 'Deaths', 'Predicted', long$key)
long$key <- ifelse(long$key == 'Predicted', 'Muertes\nprevistas',
                   'Muertes\nobservadas')


# Keep only Madrid, Lombardy, Emilia Romagna
long <- long %>%
  filter(country %in% c('Madrid',
                        'Lombardia',
                        'Emilia Romagna'))

ggplot(data = long,
       aes(x = days_since,
           y = value,
           lty = key,
           color = country)) +
  geom_point(data = long %>% filter(key == 'Muertes\nobservadas'), size = 2, alpha = 0.8) +
  geom_line(data = long %>% filter(key == 'Muertes\nprevistas'), size = 1, alpha = 0.7) +
  geom_line(data = long %>% filter(key != 'Muertes\nprevistas'), size = 0.8) +
  theme_simple() +
  scale_y_log10() +
  scale_linetype_manual(name ='',
                        values = c(1,4)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  # labs(x = 'Days since first day at 5 or more cumulative deaths',
  #      y = 'Deaths',
  #      title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
  #      caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
  #      subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    labs(x = 'Días desde el primer día a 5 o más muertes acumuladas',
       y = 'Muertes (escala logarítmica)',
       title = 'Muertes por COVID-19',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Tasa de crecimiento calculada desde el primer día a 5 o más muertes acumuladas)\n(Muertes "previstas": suponiendo que no hay cambios en la tasa de crecimiento)') +
    theme(strip.text = element_text(size = text_size * 0.75),
          plot.title = element_text(size = text_size * 3),
          legend.text = element_text(size = text_size * 1.5),
          axis.title = element_text(size = text_size * 2),
          axis.text = element_text(size = text_size * 2))

# cols <- c(cols, 'darkorange')
# ggplot(data = ol,
#        aes(x = days_since,
#            y = value,
#            lty = key,
#            color = key)) +
#   scale_y_log10() +
#   geom_line(aes(color = country)) +
#   
#   # geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Italy')),
#   #           size = 1.2, alpha = 0.8) +
#   #   geom_line(data = ol %>% filter(key %in% c('Lombardia')),
#   #           size = 0.5, alpha = 0.8) +
#   # geom_point(data = ol %>% filter(key == 'Deaths')) +
#   # geom_line(data = ol %>% filter(key == 'Deaths'),
#   #           size = 0.8) +
#   theme_simple() +
#   scale_linetype_manual(name ='',
#                         values = c(1,6,2)) +
#   scale_color_manual(name = '',
#                      values = cols) +
#   theme(legend.position = 'top') +
#   labs(x = 'Days since first day at >5 deaths',
#        y = 'Deaths',
#        title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
#        caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
#        subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
#     theme(strip.text = element_text(size = text_size * 1),
#           plot.title = element_text(size = 15))

# Map data preparation

if(!'map.RData' %in% dir()){
  esp1 <- getData(name = 'GADM', country = 'ESP', level = 1)
# Remove canary
esp1 <- esp1[esp1@data$NAME_1 != 'Islas Canarias',]
espf <- fortify(esp1, region = 'NAME_1')

# Standardize names
# Convert names
map_names <- esp1@data$NAME_1
data_names <- sort(unique(esp_df$ccaa))
names_df <- tibble(NAME_1 = c('Andalucía',
 'Aragón',
 'Cantabria',
 'Castilla-La Mancha',
 'Castilla y León',
 'Cataluña',
 'Ceuta y Melilla',
 'Comunidad de Madrid',
 'Comunidad Foral de Navarra',
 'Comunidad Valenciana',
 'Extremadura',
 'Galicia',
 'Islas Baleares',
 'La Rioja',
 'País Vasco',
 'Principado de Asturias',
 'Región de Murcia'),
 ccaa = c('Andalucía',
 'Aragón',
 'Cantabria',
 'CLM',
 'CyL',
 'Cataluña',
 'Melilla',
 'Madrid',
 'Navarra',
 'C. Valenciana',
 'Extremadura',
 'Galicia',
 'Baleares',
 'La Rioja',
 'País Vasco',
 'Asturias',
 'Murcia'))


espf <- left_join(espf %>% dplyr::rename(NAME_1 = id), names_df)
centroids <- data.frame(coordinates(esp1))
names(centroids) <- c('long', 'lat')
centroids$NAME_1 <- esp1$NAME_1
centroids <- centroids %>% left_join(names_df)

# Get random sampling points

  random_list <- list()
  for(i in 1:nrow(esp1)){
    message(i)
    shp <- esp1[i,]
    # bb <- bbox(shp)
    this_ccaa <- esp1@data$NAME_1[i]
    # xs <- runif(n = 500, min = bb[1,1], max = bb[1,2])
    # ys <- runif(n = 500, min = bb[2,1], max = bb[2,2])
    # random_points <- expand.grid(long = xs, lat = ys) %>%
    #   mutate(x = long,
    #          y = lat)
    # coordinates(random_points) <- ~x+y
    # proj4string(random_points) <- proj4string(shp)
    # get ccaa
    message('getting locations of randomly generated points')
    # polys <- over(random_points,polygons(shp))
    # polys <- as.numeric(polys)
    random_points <- spsample(shp, n = 20000, type = 'random')
    random_points <- data.frame(random_points)
    random_points$NAME_1 <-  this_ccaa
    random_points <- left_join(random_points, names_df) %>% dplyr::select(-NAME_1)
    random_list[[i]] <- random_points
  }
  random_points <- bind_rows(random_list)
  random_points <- random_points %>% mutate(long = x,
                                            lat = y)

save(espf,
     esp1,
     names_df,
     centroids,
     random_points,
     file = 'map.RData')
} else {
  load('map.RData')
}

# Define a function for adding zerio
add_zero <- 
  function (x, n) 
  {
    x <- as.character(x)
    adders <- n - nchar(x)
    adders <- ifelse(adders < 0, 0, adders)
    for (i in 1:length(x)) {
      if (!is.na(x[i])) {
        x[i] <- paste0(paste0(rep("0", adders[i]), collapse = ""), 
                       x[i], collapse = "")
      }
    }
    return(x)
  }

remake_world_map <- FALSE
options(scipen = '999')
if(remake_world_map){
  # World map animation
  world <- map_data('world')
  # world <- ne_countries(scale = "medium", returnclass = "sf")
  
  # Get plotting data
  pd <- df_country %>%
    dplyr::select(date, lng, lat, n = cases)
  dates <- sort(unique(pd$date))
  n_days <- length(dates)
  # # Define vectors for projection
  # vec_lon <- seq(30, -20, length = n_days)
  # vec_lat <- seq(25, 15, length = n_days)
  
  dir.create('animation')
  for(i in 1:n_days){
    message(i, ' of ', n_days)
    this_date <- dates[i]
    # this_lon <- vec_lon[i]
    # this_lat <- vec_lat[i]
    # the_crs <-
    #   paste0("+proj=laea +lat_0=", this_lat,
    #          " +lon_0=",
    #          this_lon,
    #          " +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs ")
    sub_data <- pd %>%
      filter(date == this_date)
    # coordinates(sub_data) <- ~lng+lat
    # proj4string(sub_data) <- proj4string(esp1)
    # # sub_data <- spTransform(sub_data,
    # #                         the_crs)
    # coordy <- coordinates(sub_data)
    # sub_data@data$long <- coordy[,1]
    # sub_data@data$lat <- coordy[,2]
  
    g <- ggplot() +
      geom_polygon(data = world,
                   aes(x = long,
                       y = lat,
                       group = group),
                   fill = 'black',
                   color = 'white',
                   size = 0.1) +
      theme_map() +
          geom_point(data = sub_data %>% filter(n > 0) %>% mutate(Deaths = n),
                 aes(x = lng,
                     y = lat,
                     size = Deaths),
                 color = 'red',
                 alpha = 0.6) +
      geom_point(data = tibble(x = c(0,0), y = c(0,0), Deaths = c(1, 100000)),
                 aes(x = x,
                     y = y,
                     size = Deaths),
                 color = 'red',
                 alpha =0.001) +
      scale_size_area(name = '', breaks = c(100, 1000, 10000, 100000),
                      max_size = 25
                      ) +
    # scale_size_area(name = '', limits = c(1, 10), breaks = c(0, 10, 30, 50, 70, 100, 200, 500)) +
      labs(title = this_date) +
      theme(plot.title = element_text(size = 30),
            legend.text = element_text(size = 15),
            legend.position = 'left')
  
    plot_number <- add_zero(i, 3)
    ggsave(filename = paste0('animation/', plot_number, '.png'),
           plot = g,
           width = 9.5,
           height = 5.1)
  }
  setwd('animation')
  system('convert -delay 30x100 -loop 0 *.png result.gif')
  setwd('..')

}

Maps of Spain

make_map <- function(var = 'deaths',
                     data = NULL,
                     pop = FALSE,
                     pop_factor = 100000,
                     points = FALSE,
                     line_color = 'white',
                     add_names = T,
                     add_values = T,
                     text_size = 2.7){
  
  if(is.null(data)){
    data <- esp_df %>%  mutate(ccaa = cat_transform(ccaa))

  }

  left <- espf %>%   mutate(ccaa = cat_transform(ccaa)) 
  right <- data[,c('ccaa', paste0(var, '_non_cum'))]
  

  names(right)[ncol(right)] <- 'var'
  right <- right %>% group_by(ccaa) %>% summarise(var = sum(var, na.rm = T))
  
  if(pop){
    right <- left_join(right, esp_pop)
    right$var <- right$var / right$pop * pop_factor
  }
  map <- left_join(left, right)
  
  if(points){
    the_points <- centroids %>%
      left_join(right)
    g <- ggplot() +
      geom_polygon(data = map,
         aes(x = long,
             y = lat,
             group = group),
         fill = 'black',
         color = line_color,
         lwd = 0.4, alpha = 0.8) +
      geom_point(data = the_points,
                 aes(x = long,
                     y = lat,
                     size = var),
                 color = 'red',
                 alpha = 0.7) +
      scale_size_area(name = '', max_size = 20)
  } else {
    # cols <- c('#008080','#70a494','#b4c8a8','#f6edbd','#edbb8a','#de8a5a','#ca562c')
    cols <- RColorBrewer::brewer.pal(n = 8, name = 'Blues')
    g <- ggplot(data = map,
         aes(x = long,
             y = lat,
             group = group)) +
    geom_polygon(aes(fill = var),
                 lwd = 0.3,
                 color = line_color) +
      scale_fill_gradientn(name = '',
                           colours = cols)
    # scale_fill_viridis(name = '' ,option = 'magma',
    #                    direction = -1) 
  }
  
  # Add names?
  if(add_names){
    centy <- centroids %>% left_join(right)
    if(add_values){
      centy$label <- paste0(centy$ccaa, '\n(', round(centy$var, digits = 2), ')')
    } else {
      centy$label <- centy$ccaa
    }

    g <- g +
      geom_text(data = centy,
                aes(x = long,
                    y = lat,
                    label = label,
                    group = ccaa),
                alpha = 0.7,
                size = text_size)
  }
  
  g +
    theme_map() +
    labs(subtitle = paste0('Data as of ', max(data$date))) +
    theme(legend.position = 'right')
  
}

make_dot_map <- function(var = 'deaths',
                     date = NULL,
                     pop = FALSE,
                     pop_factor = 100,
                     point_factor = 1,
                     points = FALSE,
                     point_color = 'darkred',
                     point_size = 0.6,
                     point_alpha = 0.5){
  
  
  if(is.null(date)){
    the_date <- max(esp_df$date)
  } else {
    the_date <- date
  }
    right <- esp_df[esp_df$date == the_date,c('ccaa', var)]
   names(right)[ncol(right)] <- 'var'
  if(pop){
    right <- left_join(right, esp_pop)
    right$var <- right$var / right$pop * pop_factor
  }
  map_data <- esp1@data %>%
    left_join(names_df) %>%
      left_join(right)
  map_data$var <- map_data$var / point_factor
  out_list <- list()
  for(i in 1:nrow(map_data)){
    sub_data <- map_data[i,]
    this_value = round(sub_data$var)

    if(this_value >= 1){
      this_ccaa = sub_data$ccaa
      # get some points
      sub_points <- random_points %>% filter(ccaa == this_ccaa)
      sampled_points <- sub_points %>% dplyr::sample_n(this_value)
      out_list[[i]] <- sampled_points
    }
  }
  the_points <- bind_rows(out_list)
  
  g <- ggplot() +
    geom_polygon(data = espf,
         aes(x = long,
             y = lat,
             group = group),
         fill = 'white',
         color = 'black',
         lwd = 0.4, alpha = 0.8) +
    geom_point(data = the_points,
               aes(x = long,
                   y = lat),
               color = point_color,
               size = point_size,
               alpha = point_alpha)
  g +
    theme_map() +
    labs(subtitle = paste0('Data as of ', max(esp_df$date)))
  
}

Deaths

Absolute number of deaths: points

make_map(var = 'deaths',
       points = T) +
  labs(title = 'Number of deaths',
       caption = '@joethebrew')

Absolute number of deaths: choropleth

make_map(var = 'deaths',
         line_color = 'darkgrey',
       points = F) +
  labs(title = 'Number of deaths',
       caption = '@joethebrew')

Number of deaths adjusted by population: points

make_map(var = 'deaths', pop = TRUE, points = T) +
  labs(title = 'Number of deaths per 100,000',
       caption = '@joethebrew')

Number of deaths adjusted by population: polygons

make_map(var = 'deaths', pop = TRUE, points = F, line_color = 'darkgrey') +
  labs(title = 'Number of deaths per 100,000',
       caption = '@joethebrew')

Number of deaths: 1 dot per death

make_dot_map(var = 'deaths', point_size = 0.05) +
  labs(title = 'COVID-19 deaths: 1 point = 1 death\nImportant: points are random within each CCAA; do not reflect exact location',
       caption = '@joethebrew')

Cases

Absolute number of cases: points

make_map(var = 'cases',
       points = T) +
  labs(title = 'Number of confirmed cases',
       caption = '@joethebrew')

Absolute number of cases: choropleth

make_map(var = 'cases',
         line_color = 'darkgrey',
       points = F) +
  labs(title = 'Number of confirmed cases',
       caption = '@joethebrew')

Number of cases adjusted by population: points

make_map(var = 'cases', pop = TRUE, points = T) +
  labs(title = 'Number of confirmed cases per 100,000',
       caption = '@joethebrew')

Number of cases adjusted by population: polygons

make_map(var = 'cases', pop = TRUE, points = F,
         line_color = 'darkgrey') +
  labs(title = 'Number of confirmed cases per 100,000',
       caption = '@joethebrew')

Number of cases: points

make_dot_map(var = 'cases',
             point_size = 0.05, point_alpha = 0.5, point_factor = 10) +
  labs(title = 'COVID-19 cases: 1 point = 10 cases\nImportant: points are random within each CCAA; do not reflect exact location',
       caption = '@joethebrew')